ISWC2012 Session Chair 2 Palmonari Matteo 48a74a474d907edb77a0cdb272adea5767df0be2 University of Milano-Bicocca Matteo Palmonari Palmonari Matteo Mari Carmen Mari Carmen Suárez-Figueroa f9494ff417ea83c9d0fb4ddb0f4bb8b2c76b0b22 Suárez-Figueroa Mari Carmen Suárez-Figueroa Universidad Politecnica de Madrid ISWC2012 Session Chair Mounir Mokhtari d9dca0483d8e3ef877c81510e0e396805f910c41 Image & Pervasive Access Lab (IPAL), UMI CNRS Mokhtari Mounir Mounir Mokhtari Monash University Monash University Sambhawa Priya 974b04e6905f6a70f94024e5973cb6722ea9ef60 Lehigh University Priya Sambhawa Sambhawa Priya VU Amsterdam Willem Robert 8985bbc3abd3830d25e6ed541b7bb1942eb85c27 Van Hage Willem Robert Van Hage Willem Robert Van Hage In this paper, we present a doctoral thesis which introduces a new approach of time series enrichment with semantics. The paper shows the problem of assigning time series data to the right party of interest and why this problem could not be solved so far. We demonstrate a new way of processing semantic time series and the consequential ability of addressing users. The combination of time series processing and Semantic Web technologies leads us to a new powerful method of data processing and data generation, which offers completely new opportunities to the expert user. A Multi-Domain Framework for Community Building Based on Data Tagging A Multi-Domain Framework for Community Building Based on Data Tagging Bojan Bozic 76500430 A Multi-Domain Framework for Community Building Based on Data Tagging 76500430 Bernardo Magnini e0676e9f8b3753600d1a31d11da3abc1d40c3281 Fondazione Bruno Kessler Magnini Bernardo Bernardo Magnini David Karger MIT Karger David David Karger Naoki Fukuta 460aee02e16bc8ef5d71fc487dc4654ee22e8b9e Shizuoka University Fukuta Naoki Naoki Fukuta Conor Hayes e3e11c1ce25fbaccc08ed9085e4ccd882a79a8f1 DERI, NUI Galway Hayes Conor Conor Hayes GESIS - Leibniz Institute for the Social Sciences GESIS - Leibniz Institute for the Social Sciences Naimdjon Takhirov 2108ae71bf373f4d15c01a5a18fff20ae0b5c60a Norwegian University of Science and Technology Takhirov Naimdjon Naimdjon Takhirov 76500294 Linked Stream Data Processing Engines: Facts and Figures Linked Stream Data Linked Stream Data Processing Engines: Facts and Figures Linked Stream Data Benchmarking Danh Le Phuoc, Minh Dao-Tran, Minh-Duc Pham, Peter Boncz, Thomas Eiter and Michael Fink Benchmarking Linked Stream Data Processing Linked Stream Data Processing Engines: Facts and Figures 76500294 Linked Stream Data,Linked Stream Data Processing,Benchmarking Linked Stream Data, i.e., the RDF data model extended for representing stream data generated from sensors social network applications, is gaining popularity. This has motivated considerable work on developing corresponding data models associated with processing engines. However, current implemented engines have not been thoroughly evaluated to assess their capabilities. For reasonable systematic evaluations, in this work we propose a novel, customizable evaluation framework and a corresponding methodology for realistic data generation, system testing, and result analysis. Based on this evaluation environment, extensive experiments have been conducted in order to compare the state-of-the-art LSD engines wrt. qualitative and quantitative properties, taking into account the underlying principles of stream processing. Consequently, we provide a detailed analysis of the experimental outcomes that reveal useful findings for improving current and future engines. Linked Stream Data Processing David Huynh David Huynh 686042aaadc71e7514c0e7fefaf5c15d32abc9d2 David Huynh Google Matthias Thimm a46d3da4820a0df9ba0426479e35331985557bfd University of Koblenz and Landau Thimm Matthias Matthias Thimm Thomas Hubauer 8fb159e64b68a46283679910a3afba8a0ce8da53 Siemens AG, Corporate Technology Hubauer Thomas Thomas Hubauer ISWC2012 Session Chair Clark & Parsia Clark & Parsia 1 Nick 8c1f1d8aa5613b419a5f3716110ce9dcd3a8946f Bassiliades Nick Bassiliades Nick Bassiliades Aristotle University of Thessaloniki Paolo Ciancarini f2918e17b4b356eb9731b54b0fd710dbfede6681 University of Bologna Ciancarini Paolo Paolo Ciancarini ISWC2012 Session Chair Thomas Schandl a800b79dc962704d9731653a26e0b45f744674ba Semantic Web Company GmbH Schandl Thomas Thomas Schandl Workshop Chair Tudor University of Queensland Tudor Groza Groza 2ddece58ea61e1d34878e723a8af024242daec80 Tudor Groza 3 Jans Aasman 203216e2412e287bad61ff368f0b51f41cc73311 Franz Aasman Jans Jans Aasman Ryutaro Ichise 0a09da4b327971fff4a6573caac2b4872cab5cc9 National Institute of Informatics Ichise Ryutaro Ryutaro Ichise ISWC2012 Session Chair Makoto Nakatsuji 676bbe0a08c39b31531c2543d7d3a6cd11ee1090 NTT Nakatsuji Makoto Makoto Nakatsuji University of New South Wales University of New South Wales National Observatory of Athens National Observatory of Athens Harith ad0c7d68490b84d6c7f8b0cb8aa1e457559386ef Harith Alani Harith Alani Alani KMi, The Open University CERN CERN Krishnaprasad Thirunarayan 4244f1f45d6d9bb6d4a3e608db0d856e1d005715 Thirunarayan Krishnaprasad Thirunarayan Krishnaprasad e70f6742afd3d6c1b6eebb41e0ae7cfa1f150108 Kno.e.sis Center, Wright State University OCLC OCLC University of Texas at Dallas University of Texas at Dallas University of Maryland Louiqa Raschid Louiqa Raschid Louiqa Raschid 26e37b130fe1f4cfce90f9db362074b90b9e6de2 Siemens AG Österreich Siemens AG Österreich 16795 2012-11-11T16:37:43-0800 iswc2012 metadata v20121111 2012-11-12T09:00:00+05:00 Scalability Linked Data Linked Science Scalability Big Data Open Science The 2nd International Workshop on Linked Science 2012—Tackling Big Data Open Science Linked Data Linked Science Scientific communication has traditionally relied upon publications and presentations, with an estimate of millions of publications worldwide per year; the growth rate of PubMed alone is now 1 paper per minute. The results described in these articles are often backed by large amounts of diverse data produced by complex experiments, computer simulations, and observations of physical phenomena. Because of this avalanche of data, it is increasingly hard to validate, reproduce, reuse and leverage scientific data. In addition, although publications, methods and datasets are very related, they are not easily accessible and interlinked. The notable exception is omics research where journals require deposit of sequences in databanks as a condition of publication. Even where data is discoverable and accessible, significant challenges remain in data reuse and sharing, in facilitating the necessary correlation, integration and synthesis of data across levels of theory, techniques and disciplines. In the 2nd International Workshop on Linked Science (LISC2012) we will discuss and present results of new ways of publishing, sharing and linking scientific data together, and reasoning over such data to discover interesting new links to validate research. The theme of this year’s workshop will focus on research addressing these issues with respect to big data. Big Data is loosely characterized by the size and/or number of individual files, the number of represented variables, a range of physical scales, a range of scientific disciplines, heterogeneous metadata and data formats, in short data that cannot easily be accessed and manipulated from a thumb-drive. 2012-11-12T17:30:00+05:00 Linked Science, Big Data, Linked Data, Open Science, Scientific Communication, Scalability LISC-2012 Scientific Communication Scientific Communication Big Data Kristian Slabbekoorn da200b97b2b21aaf4a5412730368cd88137dc3b7 Tokyo Institute of Technology Slabbekoorn Kristian Kristian Slabbekoorn Veronica Rizzi University of Trento Rizzi Veronica Veronica Rizzi University of Illinois at Chicago University of Illinois at Chicago Daniel Bär 066e6b9ecbe25dddc46b3fc71e51f161cfb6ed6f TU Darmstadt Bär Daniel Daniel Bär Mohsen Taheriyan c4790d48328a9f0b42b299a6b6135c0cb9ce7c4e Information Sciences Institute, University of Southern California Taheriyan Mohsen Mohsen Taheriyan National and Kapodistrian University of Athens a45138009890ce780bd31806e8888b8a2f26d1af Kostis Kyzirakos Kostis Kyzirakos Kostis Kyzirakos forward look forward look 2 1 CMU CMU 35a8d4858ba240996a6f71836d93fbfdcd2b4843 Axel Siemens AG Österreich; DERI, NUI Galway Axel Polleres Polleres Axel Polleres Universidade Nova de Lisboa Universidade Nova de Lisboa RDFS inference,Parallel reasoning,Scalable reasoning Parallel reasoning RDFS inference Reasoning in RDFS is Inherently Serial, at least in the worst case Although it appears that reasoning in RDFS is embarrassingly parallel, this is not the case. Because all vocabulary is treated the same way in RDF, it is possible to extend the RDFS ontology vocabulary. The ability permits the creation of useful constructs that are not amenable to parallelism, and that in the end require serial processing. RDFS inference Reasoning in RDFS is Inherently Serial, at least in the worst case Peter Patel-Schneider Parallel reasoning Scalable reasoning Reasoning in RDFS is Inherently Serial, at least in the worst case Scalable reasoning University of Koblenz and Landau University of Koblenz and Landau ISWC2012 Session Chair Microsoft Research Microsoft Research Dario Cerizza f12618752715ccd047ba2db8e6f97087a50502a9 Politecnico di Milano Cerizza Dario Dario Cerizza Nokia Services Nokia Services Haofen Shanghai Jiao Tong University Haofen Wang 0233b9fd11287c2f4beecdd5a3002eb5cae9f080 Wang Haofen Wang Hepp 49e06491d1c02eead2d362e2300fa56d24ed5213 Martin Hepp Martin Bundeswehr University Munich Martin Hepp 3 Edna Ruckhaus 8d09d177c2154e6e9e32d56c86a6d64d302afcac Universidad Simon Bolivar Edna Edna Ruckhaus Ruckhaus Knowledge Pattern Extraction and their usage in Exploratory Search Andrea Giovanni Nuzzolese 76500438 Knowledge Pattern Extraction and their usage in Exploratory Search Knowledge interaction in Web context is a challenging problem. For instance, it requires to deal with complex structures able to filter knowledge by drawing a meaningful context boundary around data. We assume that these complex structures can be formalized as Knowledge Patterns (KPs), aka frames. This Ph.D. work is aimed at developing methods for extracting KPs from the Web and at applying KPs to exploratory search tasks. We want to extract KPs by analyzing the structure of Web links from rich resources, such as Wikipedia. 76500438 Knowledge Pattern Extraction and their usage in Exploratory Search Amar Djalil Mezaour 61370d2392cdeb675cf36c3dacbe74dbd28d686f EXALEAD, Dassault Systèmes Mezaour Amar Djalil Amar Djalil Mezaour John Google John Gianandrea John Gianandrea John Giannandrea, Director of Engineering at Google, leads the http://www.freebase.com/ project, an open database of knowledge which anyone can contribute to. Freebase was created by Metaweb Technologies, which John founded and which was acquired by Google in 2010. Prior to Metaweb, John co-founded Tellme Networks and was the chief technologist of Netscape’s browser group where he contributed to many industry standards including HTML, HTTP, SSL, Java and RDF. John is originally from Scotland and graduated from Strathclyde University, Glasgow. Gianandrea Antidot Fabrice Lacroix Fabrice Lacroix cc3e785d5647ada02b76fa329db62950daa3d2d1 Lacroix Fabrice Alexander Borgida d06a067f069937274cca6c2b03a5330b71d660cb Rutgers University Borgida Alexander Alexander Borgida Cabot Senior PC Member at ISWC2012(Research Track) SAP SAP Processing of real-time data streams has become a very important mechanism in many application areas: Smart cities, Smart grid, eHealth, to name but a few. Although semantic technologies have been recognized as one of very important enablers for this type of applications, there are still several challenges to be resolved in order to apply them in real-world applications. Two most important challenges are: an expressive query language that enables the description of complex situations to be detected (considering real-time and historical data) and an efficient asynchronous retrieval mechanism for the large distributed data streams. Beside elaborating on the importance of these challenges for the real-time aware applications, in this tutorial we give an overview of existing approaches for the semantic processing of real-time streams and present a novel approach based on the recent development in two emerging research areas, Complex Event Processing, that enables an efficient in memory processing of huge real-time data and Cloud Computing, that supports an elastic storage of enormous data volumes. Two studies will be demonstrated: We will show conceptual and technical details of the public-available Platform PLAY, that realizes presented approach. We will demonstrate its usage through two applications that combine real-time events coming from Smartphone (geo position, incoming /outgoing /missing calls), Social media (Facebook wall updates and recent tweets) and real-time sensors. The approach for writing semantic adapters for new event sources and semantic patterns to be detected in event streams will be introduced. 2012-11-12T14:00:00+05:00 2012-11-12T17:30:00+05:00 Streams Scalable semantic processing of huge, distributed real-time streams: Semantics Between Event Processing and Cloud Computing Razan Paul 9219c0dc7eb89e6e01f45fd3da9da5446e8beec5 University of Queensland Paul Razan Razan Paul 3 Bo Fu University of Victoria Fu Bo Bo Fu 2 Kendall Clark 1cde8114566a4ec3c99aeee2aab07bebf5cdc768 Clark & Parsia Clark Kendall Kendall Clark University of Trento University of Trento Claus Stadler University of Leipzig Stadler Claus Claus Stadler University of Kentucky University of Kentucky ISWC2012 Session Chair University of California, Davis University of California, Davis 1 Steffen Steffen Staab Steffen Staab ae8f32f31b69df2872d4c5d2e3bf21cf09cadf90 Staab University of Koblenz and Landau ISWC2012 Session Chair Planning In real world cases, building reliable problem centric views over Linked Data is a challenging task. An ideal method should include a formal representation of the requirements of the needed dataset and a controlled process moving from the original sources to the outcome. We believe that a goal oriented approach, similar to the AI planning problem, could be successful in controlling the process of linked data fusion, as well as to formalize the relations between requirements, process and result. Linked Data Planning Towards a theoretical foundation for the harmonization of linked data 76500434 Linked Data, Data Harmonization, Planning Linked Data 76500434 Data Harmonization Towards a theoretical foundation for the harmonization of linked data Enrico Daga Data Harmonization Towards a theoretical foundation for the harmonization of linked data Jaroslav Kuchar 032105758485339f4f1d530c3b61af5c11b5e3d9 Czech Technical University in Prague Kuchar Jaroslav Jaroslav Kuchar National and Kapodistrian University of Athens 3d5c1f8e68b6329ae8f6f6b76102c0b33ad72ab5 George Garbis George Garbis George Garbis Yahoo! Research Yahoo! Research ISWC2012 Worshop and Tutorial Chair Exeter Stuart Linked Data have by-and-large been designed around centralized, powerful Web servers and the (mobile) clients accessing them. As a direct consequence of these design decisions, the usage of data- sharing technologies depends on the availability of a Web infrastructure comprised of data-centers, high-speed, reliable Internet connections, and modern client devices. Four-billion people currently have no access to such an infrastructure and are thus deprived of the benefits Linked (Open) Data provides. This tutorial will show how the design principles and technologies of Linked Data can be adapted to distributed networks, and thus contribute to closing this “digital data divide”. Join us to learn how not to forget the majority of the world population when thinking of the potential users of Linked Data and discuss the challenges this represents for the research community. ICT4D, Linked Data, Global change, Digital divide ICT4D Global change Linked Data LD4D: Linked Data for Development Digital divide Linked Data Global change ICT4D Digital divide 2012-11-12T17:30:00+05:00 2012-11-12T09:00:00+05:00 LD4D Discoverability Discoverability,Open Educational Resources,Linked Data,Recommandation,Named Entity Recognition,Application,Summarisation Summarisation DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries We demonstrate the DiscOU engine implementing a resource discovery approach where the textual components of open educational resources are automatically annotated with relevant entities (using a named entity recognition system), so that these rich annotations can be searched by similarity, based on existing resources of interest. Open Educational Resources Named Entity Recognition Linked Data Recommandation Summarisation Discoverability Recommandation DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries Application Open Educational Resources Linked Data Mathieu d'Aquin, Carlo Allocca and Trevor Collins Named Entity Recognition Application DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries Hypios Hypios Free University of Bozen-Bolzano Mariano Rodriguez-Muro Mariano Rodriguez-Muro 9834233dbc9d00654ac36e085f897a38c1a88df3 Mariano Rodriguez-Muro 3 Franconi cc31df1716defe7e7094b4e019d2218078072e53 Enrico Enrico Franconi Free University of Bozen-Bolzano Enrico Franconi Chris Chaulk 5b51949c829f9c0d7405e7e19a79fd56dc65ef49 EMC Corporation Chaulk Chris Chris Chaulk Tim Clark d9c9efcbcb2877d085cff13ec8b748772fec0ed6 Massachusetts General Hospital Clark Tim Tim Clark 2183f513ec0eec352b179b9dfa131c7a37b945ad Image & Pervasive Access Lab (IPAL), UMI CNRS Thibaut Tiberghien Thibaut Tiberghien Thibaut Tiberghien University of Grenoble University of Grenoble Martin Theobald 6946bbceaf6bac12a8e5029301ef7eac3c221839 Max Planck Institute for Informatics Theobald Martin Martin Theobald Hans Vasquez-Gross 414d78cf3661498dab056a741c8aa5b8f61e79c2 University of California, Davis Vasquez-Gross Hans Hans Vasquez-Gross Magnus Knuth 2f6f063a903efc8fa6c05e51d51ae30bb82ab385 Hasso Plattner Institute Knuth Magnus Magnus Knuth University of Zurich Abraham Bernstein Abraham Bernstein Abraham Bernstein Richard Cyganiak DERI, NUI Galway Cyganiak Richard Richard Cyganiak 1 Stanford University Stanford University 3 Manuel Atencia 1a955294e7f96dc652cb0622b88d256b8765bda7 INRIA & University of Grenoble Atencia Manuel Manuel Atencia DERI, NUI Galway Vit Novacek Novacek Vit c91b8f5b6908408264728371f7331249e3857a87 Vit Novacek Linked Data Data publication Licensing,Linked Data,Data publication Serena Villata and Fabien Gandon Towards Licenses Compatibility and Composition in the Web of Data We propose a general framework to attach the licensing terms to the data where the compatibility of the licensing terms concerning the data affected by a query is verified, and, if compatible, the licenses are combined into a composite license. The framework returns the composite license as licensing term about the data resulting from the query. Linked Data Licensing Towards Licenses Compatibility and Composition in the Web of Data Data publication Licensing Towards Licenses Compatibility and Composition in the Web of Data Universitat de Lleida Universitat de Lleida Jie Liu 88b1a16c736d1117acebcfb8035cba651d54f3c8 UALR Liu Jie Jie Liu Justifications Justifications 76500282 Justification Finding Extracting Justifications from BioPortal Ontologies Justification Finding Blackbox Debugging Matthew Horridge, Bijan Parsia and Ulrike Sattler This paper presents an evaluation of state of the art black box justification finding algorithms on the NCBO BioPortal ontology corpus. This corpus represents a set of naturally occurring ontologies that vary greatly in size and expressivity. The results paint a picture of the performance that can be expected when finding all justifications for entailments using black box justification finding techniques. The results also show that many naturally occurring ontologies exhibit a rich justificatory structure, with some ontologies having extremely high numbers of justifications per entailment. Justifications,Justification Finding,Explanation,Blackbox Debugging Extracting Justifications from BioPortal Ontologies Extracting Justifications from BioPortal Ontologies Blackbox Debugging Explanation 76500282 Explanation 76500446 76500446 Online Unsupervised Coreference Resolution for Semi-Structured Heterogeneous Data A pair of RDF instances are said to corefer when they are intended to denote the same thing in the world, for example, when two nodes of type foaf:Person describe the same individual. This problem is central to integrating and inter-linking semi-structured datasets. We are developing an online, unsupervised coreference resolution framework for heterogeneous, semi-structured data. The online aspect requires us to process new instances as they appear and not as a batch. The instances are heterogeneous in that they may contain terms from different ontologies whose alignments are not known in advance. Our framework encompasses a two-phased clustering algorithm that is both flexible and distributable, a probabilistic multidimensional attribute model that will support robust schema mappings, and a consolidation algorithm that will be used to perform instance consolidation in order to improve accuracy rates over time by addressing data sparseness. Online Unsupervised Coreference Resolution for Semi-Structured Heterogeneous Data Jennifer Sleeman Online Unsupervised Coreference Resolution for Semi-Structured Heterogeneous Data 1 University of Bologna University of Bologna Fausto Guinchiliglia University of Trento Guinchiliglia Fausto Fausto Guinchiliglia Andreas Thalhammer 6f575397b076c84395e15262ac5339f249ad0fb4 University of Innsbruck Thalhammer Andreas Andreas Thalhammer Stavropoulos Thanos G. Stavropoulos Aristotle University of Thessaloniki d1d69c3d1cc349f98825dde21d315ddb8c11fabe Thanos G. Thanos G. Stavropoulos Fuzzy ontologies Uncertainty reasoning 8th International Workshop on Uncertainty Reasoning for the Semantic Web Uncertainty representation Fuzzy ontologies Imperfect knowledge 2012-11-11T17:30:00+05:00 Probabilistic ontologies Imperfect knowledge Uncertainty reasoning, Uncertainty representation, Imperfect knowledge, Probabilistic ontologies, Fuzzy ontologies Probabilistic ontologies Uncertainty representation URSW Uncertainty reasoning 2012-11-11T09:00:00+05:00 This workshop will discuss different approach to deal with uncertainty (in a wide sense) in the Semantic Web. Uncertainty is an unavoidable factor in several processes, such as knowledge interchange and application interoperability, and Semantic Web data, which are usually incomplete, inconsistent, and inaccurate. This suggests the need to apply different formalisms (probability, fuzzy logic, decision theory, etc.) to enrich Semantic Web technologies and applications. Bioinformatics at Centre for Plant Biotechnology and Genomics UPM-INIA Bioinformatics at Centre for Plant Biotechnology and Genomics UPM-INIA University of Leipzig University of Leipzig Mikko Rinne Complex event processing SPARQL Update for Complex Event Processing SPARQL Update for Complex Event Processing SPARQL Complex event processing SPARQL RDF Complex event processing, SPARQL, RDF, Rete-algorithm Rete-algorithm Rete-algorithm 76500442 SPARQL Update for Complex Event Processing RDF Complex event processing is currently done primarily with proprietary definition languages. Future smart environments will require collaboration of multi-platform sensors operated by multiple parties. The goal of my research is to verify the applicability of standard-compliant SPARQL for complex event processing tasks. If successful, semantic web standards RDF, SPARQL and OWL with their established base of tools have many other benefits for event processing including support for interconnecting disjoint vocabularies, enriching event information with linked open data and reasoning over semantically annotated content. A software platform capable of continuous incremental evaluation of multiple parallel SPARQL queries is a key enabler of the approach. 76500442 Leigh Dodds 1bca73e5c6916c738d6ec7cc0597ad0e395e7ace Ingenta Dodds Leigh Leigh Dodds Information Sciences Institute, University of Southern California Information Sciences Institute, University of Southern California Université Libre de Bruxelles Université Libre de Bruxelles AKSW AKSW Lina Zhou University of Maryland Baltimore County Zhou Lina Lina Zhou Josef Hardi 04f2b90522cae30f692de284a130cea1762a409c Free University of Bozen-Bolzano Hardi Josef Josef Hardi 001e4d4a00b9bf4295967dcc0d3840c147704c63 Alessio Palmero Aprosio Fondazione Bruno Kessler Alessio Palmero Aprosio Alessio Palmero Aprosio 1 2 Yongchun Xu 5ac35e64c1fb85d5d1442a6013cedf6187a623df FZI Research Center for Information Technology Xu Yongchun Yongchun Xu Osaka University Osaka University Rete INSTANS: High-Performance Event Processing with Standard RDF and SPARQL Rete,SPARQL,RDF,Complex event processing INSTANS: High-Performance Event Processing with Standard RDF and SPARQL SPARQL Complex event processing Smart environments require collaboration of multi-platform sensors operated by multiple parties. Proprietary event processing solutions do not have enough interoperation flexibility, easily leading to overlapping functions wasting hardware and software resources as well as data communications. Our goal is to verify the applicability of standard-compliant SPARQL for any complex event processing task. If found feasible, semantic web methods RDF, SPARQL and OWL have the built-in support for interconnecting disjoint vocabularies, enriching event information with linked open data and reasoning over semantically annotated content, yielding a very flexible event processing environment. Our approach is designed to meet these requirements. Our INSTANS platform based on continuous execution of interconnected SPARQL queries using the Rete-algorithm is a new approach showing improved performance for event processing tasks over current SPARQL-based solutions. SPARQL Mikko Rinne, Esko Nuutila and Seppo Törmä INSTANS: High-Performance Event Processing with Standard RDF and SPARQL RDF Complex event processing RDF Rete Miranker Daniel University of Texas at Austin Daniel Miranker 0fea94336fe97522ad08d3100f2555d8b1acab05 Daniel Miranker Orri Erling 2d8d880652d4a0c0beba9ea1f96eab72ba83b10d OpenLink Erling Orri Orri Erling ISWC2012 Session Mediator Bielefeld University Bielefeld University Serena Villata 200fa053a13df35295f805b07e244b0922bb0010 INRIA Villata Serena Serena Villata 2 Hendler Jim Hendler Jim Jim Hendler b03a8c07c75cce97ff800d07844b75fd3a12f9ae RPI Annika Hinze 8db2a25da9254fe29f683ecf7ca66f5111684604 University of Waikato Hinze Annika Annika Hinze James Fan 0212761867f0f75b22330290eab1c76576fa3a97 IBM Research Fan James James Fan OpenLink OpenLink Sandia National Laboratories Sandia National Laboratories 1 Kemafor Anyanwu Anyanwu Kemafor Anyanwu North Carolina State University Kemafor 8caa6f23c7e9f622ab0b047a4850f1b32a505ae0 Kai-Uwe Sattler 5a074b484cb48f9056d70c9d136a3c6a296346e0 TU Ilmenau Sattler Kai-Uwe Kai-Uwe Sattler 3 John Yu eec594a4f3e4a9cf28a0a21f6bca8f4b90108cd0 University of California, Davis Yu John John Yu Carlo Curino Yahoo! Research Curino Carlo Carlo Curino 71a761b4b91daced90923b5fc2d37f7afeb2c501 Nuzzolese University of Bologna;STLab, ISTC-CNR Andrea Andrea Giovanni Nuzzolese Andrea Giovanni Nuzzolese ISWC2012 Session Chair Papoutsis 2633e011aa5f6af7d07067425939fdcb7ca08620 Ioannis Papoutsis Ioannis Ioannis Papoutsis National Observatory of Athens ISWC2012 Session Chair Data Platform SPUD: Semantic Processing of Urban Data Urban Integration Diagnosis We present SPUD, a semantic environment for cataloguing, exploring, integrating, understanding, processing and transforming urban information. Such information comes from the Web, government authorities, social networks and Linked Data sources. We identify a scenario consisting of the following cases: (a) Publication of a dataset, focusing on privacy protection and semantic annotation of the dataset, (b) Reporting and Consolidation of multi-faceted information, focusing on searching and visualizing heterogenous data from several sources, including social media, linked data and government data, (c) Diagnosis, based on highly expressive reasoning. Through our demonstration, we show that semantic technologies can be used to obtain business results in an environment with hundreds of heterogenous real datasets coming from different data sources and spanning multiple domains. Diagnosis Integration Urban Data Semantic Semantic Urban,Semantic,Integration,Diagnosis,Platform,Data Spyros Kotoulas, Vanessa Lopez, Raymond Lloyd, Marco Luca Sbodio, Freddy Lecue, Martin Stephenson, Elizabeth Daly, Veli Bicer, Aris Gkoulalas-Divanis, Giusy Di Lorenzo, Anika Schumann and Pol Mac Aonghusa Platform SPUD: Semantic Processing of Urban Data SPUD: Semantic Processing of Urban Data Kathryn Laskey Kathryn Laskey Kathryn 80300d4d62a9c1e49456c5406fd4746d6f73ad35 George Mason University Laskey 3 Marine Biological Laboratory Marine Biological Laboratory entity summarization 76500342 entity summarization,property ranking,evaluation,linked data,quiz game,games with a purpose Evaluating Entity Summarization Using a Game-Based Ground Truth Andreas Thalhammer, Magnus Knuth and Harald Sack In recent years, strategies for Linked Data consumption have caught attention in Semantic Web research. For direct consumption by users, Linked Data mashups, interfaces, and visualizations have become a popular research area. Many approaches in this field aim to make Linked Data interaction more user friendly to improve its accessibility for nontechnical users. A subtask for Linked Data interfaces is to present entities and their properties in a concise form. In general, these summaries take individual attributes and sometimes user contexts and preferences into account. But the objective evaluation of the quality of such summaries is an expensive task. In this paper we introduce a game-based approach aiming to establish a ground truth for the evaluation of entity summarization. We exemplify the applicability of the approach by evaluating two recent summarization approaches. Evaluating Entity Summarization Using a Game-Based Ground Truth 76500342 property ranking entity summarization linked data evaluation games with a purpose linked data games with a purpose evaluation quiz game Evaluating Entity Summarization Using a Game-Based Ground Truth quiz game property ranking 2 Christian Dirschl e69284bd9535f3f24d901076e71d135abb3cd0ab Wolters Kluwer Dirschl Christian Christian Dirschl Wim University of Sheffield Peters 360541873bc3c704abe6ed9f1a3ce13180e93740 Wim Peters Wim Peters Steven Jennings UALR Jennings Steven Steven Jennings Joanne Luciano e7660b9a608c0929b12ac5466afc5dec90fbdae5 RPI Luciano Joanne Joanne Luciano VU Amsterdam Laura Hollink Laura d6541facb723c7ec3e0df552b89a7aeb99321bb8 Laura Hollink Hollink The Trials and Tribulations of a Semantic Technology Evangelist 2012-11-15T01:00:00+05:00 2012-11-14T19:30:00+05:00 This talk will attempt to be a humorous reprise of attempts to realise the web of Open Linked Data. What have been the great successes and failures and what does the future hold? Will the empire strike back? All will be revealed in the banquet after Dinner speech. 2 Monika Solanki 006555fcc637d698a751a72c6f062a91c2fdef3b Birmingham City University Solanki Monika Monika Solanki Héctor Pérez-Urbina a6f7b3b72fbfa8608edf6e5835bb80c5e6cad7e3 Clark & Parsia Pérez-Urbina Héctor Héctor Pérez-Urbina University Carlos III of Madrid University Carlos III of Madrid Cambridge streaming linked data Tracking Movements and Attention of Crowds in Real Time Analysing Social Streams The case of London 2012 Open Ceremony twitter stream reasoning real time analysis stream reasoning Tracking Movements and Attention of Crowds in Real Time Analysing Social Streams The case of London 2012 Open Ceremony crowd attention stream reasoning,real time analysis,crowd movements,crowd attention,csparql,streaming linked data,linked data,twitter,social media,social media analysis,london2012 olympic games social media streaming linked data london2012 olympic games twitter To manage a big event require tracking in real time the movement of crowds. Solution based on mobile network data analysis are effective, but expensive. Obtaining comparable results by analysing public social stream has a clear commercial potential, especially considering that, being able to access also the content of a micro-post, the analysis can also track the attention of crowds. social media Marco Balduini and Emanuele Della Valle social media analysis csparql crowd movements crowd attention linked data real time analysis csparql linked data social media analysis london2012 olympic games Tracking Movements and Attention of Crowds in Real Time Analysing Social Streams The case of London 2012 Open Ceremony crowd movements Takahisa Fujino 1bfafa87e50ab93db7527ea7b7943751d0665ce8 Shizuoka University Fujino Takahisa Takahisa Fujino EMC Corporation EMC Corporation ISWC2012 Proceedings Chair We present QAKiS, a system for open domain Question Answering over linked data. It addresses the problem of question interpretation as a relation-based match, where fragments of the question are matched to binary relations of the triple store, using relational textual patterns automatically collected. For the demo, the relational patterns are automatically extracted from Wikipedia, while DBpedia is the RDF data set to be queried using a natural language interface. Question Answering,Natural Language Processing,Linked Data,BDpedia BDpedia Elena Cabrio, Julien Cojan, Alessio Palmero Aprosio, Bernardo Magnini, Alberto Lavelli and Fabien Gandon Linked Data Question Answering BDpedia Linked Data QAKiS: an Open Domain QA System based on Relational Patterns QAKiS: an Open Domain QA System based on Relational Patterns QAKiS: an Open Domain QA System based on Relational Patterns Natural Language Processing Natural Language Processing Question Answering 1 Sudeshna Das 357738dae0683e91b90de98b513d8b24c0baa71e Massachusetts General Hospital Das Sudeshna Sudeshna Das 2 3 DeRiVE-2012 2012-11-12T14:00:00+05:00 event exploitation social media multimedia The goal of this workshop is to strengthen the participation of the semantic web community in the recent surge of research on the use of events as a key concept for representing knowledge and organising and structuring media on the web. The workshop invites contributions to three central themes: event detection, event representation, and event exploitation. Its goal is to formulate answers to questions in these themes that advance and reflect the current state of understanding. Each submission will be expected to address at least two themes explicitly, if possible including a system demonstration. This year, we specifically invite contributions that address both event and conversation semantics in multimedia and social media. 2012-11-12T17:30:00+05:00 event representation event detection event detection event representation event exploitation Detection, Representation, and Exploitation of Events in the Semantic Web social media multimedia event detection, event representation, event exploitation, multimedia, social media Thomas Bouttaz University of Aberdeen Thomas Bouttaz Thomas Bouttaz d1cf7d8edb6c1552e3699683c6d3a90fccfcb8dc Composition of Linked Data-based RESTful Services 76500450 76500450 Composition of Linked Data-based RESTful Services Steffen Stadtmüller We address the problem of developing a scalable composition framework for Linked Data-based services, which retains the advantages of the loose coupling fostered by REST. Composition of Linked Data-based RESTful Services University of Mannheim University of Mannheim EXALEAD, Dassault Systèmes EXALEAD, Dassault Systèmes Institute for Infocomm Research Institute for Infocomm Research Francesco Osborne University of Turin Francesco Osborne 651c4a4af5ab4db99ecc207b495be0cc351e86e8 Osborne Francesco Daniel Schwabe PUC-Rio Schwabe Daniel Daniel Schwabe Julien Law-To ffbd27758de83c14456fe7a7cb238ab6f2c2f22f EXALEAD, Dassault Systèmes Law-To Julien Julien Law-To University of Bari ab740dad9a301ee0435c0d8187a4e2d905165aa7 de Gemmis Marco de Gemmis Marco de Gemmis Marco PC Member at ISWC2012(Semantic Web In Use Track) Thorsten Krueger 0f9a07ed9da2425024e704ebb8e241f45a31f33f Siemens AG Krueger Thorsten Thorsten Krueger 2012-11-14T19:30:00+05:00 2012-11-14T09:00:00+05:00 Registration Seema Sundara 6a8c49e2c1c8bcde071dde92d5377b32034794a3 Oracle Sundara Seema Seema Sundara Workshop Chair David Norheim David David Norheim Norheim 31c2448be0c03734932b4919b7278a4133a70f29 Computas a69a534c7a5802053569792e8c769d8981487eb4 Josiane Josiane Xavier Parreira DERI, NUI Galway Parreira Josiane Xavier Parreira 2bca83b0b9ef7088e67a77a8c749f97b6e072cc6 Aston University Brewster Christopher Christopher Brewster Christopher Brewster University of Bologna University of Bologna Telecom Bretagne Telecom Bretagne Humboldt University of Berlin Humboldt University of Berlin Brian Kettler Lockheed Martin Kettler Brian Brian Kettler Workshop Chair Corcho Universidad Politecnica de Madrid efbd90eca236ae3131e67f30e3abe0a1bceff305 Oscar Oscar Corcho 87476c0ef395e4ba818199fb0c1101e4d5811c2a Oscar Corcho Charalambos Kontoes 8c9f34c7ae4eaf63c5b0ba473eae819206215c9a National Observatory of Athens Kontoes Charalambos Charalambos Kontoes University of Oxford Markus Krötzsch Markus Krötzsch Markus Krötzsch 9d431a8ef18a887f519056a688c86b2d5099a188 2012-11-13T21:00:00+05:00 2012-11-13T08:30:00+05:00 Registration 643f7225974f4090aa9a7d0b3ba82e54736970eb Kapila Ponnamperuma Kapila Ponnamperuma Kapila Ponnamperuma University of Aberdeen Christophe Guéret VU Amsterdam Guéret Christophe Christophe Guéret Chenghua Lin 48ca4065e4efc565181350eee98a176a67d7e0db KMi, The Open University Lin Chenghua Chenghua Lin Concordia University Concordia University 274ee5c2434a2eb368dce6c789179aec8eb148d3 James James Pustejovsky Pustejovsky Brandeis University James Pustejovsky Maria-Esther Vidal Universidad Simon Bolivar Maria-Esther Vidal 6c9651932804d271e53f279c99d7cd1d1d429c0a Maria-Esther Vidal Workshop Chair Knowledge Modelling Inference Engine Knowledge Modelling Context Awareness Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia Context Awareness Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia Inference Engine 76500209 Ambient Assisted Living Semantic Web Semantic Web Ambient Assisted Living,Context Awareness,Knowledge Modelling,Semantic Web,Inference Engine Ambient Assisted Living Thibaut Tiberghien, Hamdi Aloulou, Mounir Mokhtari and Jit Biswas Robust solutions for ambient assisted living are numerous, yet predominantly specific in their scope of usability. In this paper, we describe the potential contribution of semantic web technologies to building more versatile solutions - a step towards adaptable context-aware engines and simplified deployments. Our conception and deployment work in hindsight, we highlight some implementation challenges and requirements for semantic web tools that would help to ease the development of context-aware services and thus generalize real-life deployment of semantically driven assistive technologies. We also compare available tools with regard to these requirements and validate our choices by providing some results from a real-life deployment. 76500209 Horridge Matthew Horridge 29c0127871609390f3d73c72c9418cc2936ac0f8 Stanford University Matthew Horridge Matthew Computas Computas KMi, The Open University Carlos Carlos Pedrinaci 7dd3dfdb407a46327824fc54ebedcc29caf1c553 Pedrinaci Carlos Pedrinaci bb20db8f12d41be62a42151b280f121194e95179 Enrico Motta Enrico Motta KMi, The Open University Enrico Motta 28a0f82609671f47d811e6bee865afb23abfb8db Jennifer Golbeck University of Maryland Golbeck Jennifer Jennifer Golbeck Xing Niu 849b1c5ff985c3098cefda9279b6c1150ed3fd32 Shanghai Jiao Tong University Niu Xing Xing Niu David Newman ebfe5ff627066f7cd366d7d08de3e8def9c0ec1f Wells Fargo Bank Newman David David Newman University of Pittstburgh University of Pittstburgh Stadtmüller Steffen Stadtmüller 547b31bc0d6de8e1cc9256b9ec77224fc28157f3 Steffen Stadtmüller Steffen AIFB, Karlsruhe Institute of Technology University of Victoria University of Victoria 7727b0b71e1bbd02c6fafb981ac417c77b0eb51d University of Oxford Thomas Thomas Lukasiewicz Thomas Lukasiewicz Lukasiewicz ea223fd1ac3921cfb6e7e515e6cfc22050b9146c In-Use Track UAMS UAMS Big Graph Data Panel 2012-11-14T14:00:00+05:00 2012-11-14T15:30:00+05:00 The Semantic Web / Linked Data has grown immensely over the past years. When the Semantic Web community started working over a decade ago the main question was where to get the data from. By now the question of how to process ever increasing amount of semantic/linked data has come to people's utmost attention. The goal of this panel is to shed light on the various approaches/options for Big Graph Data processing. Possible questions include: (1)Does the Semantic Web need any central infrastructures? (It's a Web, after all?) (2)Or will a handful of large single-owner infrastructures dominate the Semantic Web, just as they now dominate the current Web? (3)And if so, will such infrastructures be based on the standard relational model? (4)Or on MapReduce-centric key/value-pairs? (5)Is Google's (centralised) Knowledge Graph anathema to the Semantic *Web* ? (6)Are triplestore vendors just reinventing the old database wheels? (7)What is the role of clustered MapReduce-like solutions and where are their limits for processing semantic web data? Nenad Nenad Stojanovic 53a4bdea8bebaa7af9c3d002dc19b0fd88c7efdb Stojanovic Nenad Stojanovic FZI Research Center for Information Technology 2012-11-13T11:00:00+05:00 2012-11-13T10:30:00+05:00 Coffee Break Simperl AIFB, Karlsruhe Institute of Technology cb10de0e282d7010d2b7326b8c34bb59e4d3f9fa Elena Elena Simperl Elena Simperl Watson Answer Typing A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson 76500240 Question Answering Question Answering,Answer Typing,Watson 76500240 Questions often explicitly request a particular type of answer. One popular approach to answering natural language questions involves filtering candidate answers based on precompiled lists of instances of common answer types (e.g., countries, animals, foods, etc.). Such a strategy is poorly suited to an open domain in which there is an extremely broad range of types of answers, and the most frequently occurring types cover only a small fraction of all answers. In this paper we present an alternative approach called TyCor, that employs soft filtering of candidates using multiple strategies and sources. We find that TyCor significantly outperforms a single-source, single-strategy hard filtering approach, demonstrating both that multi-source multi-strategy outperforms a single source, single strategy, and that its fault tolerance yields significantly better performance than a hard filter. Watson Answer Typing A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson Question Answering Chris Welty, J William Murdock, Aditya Kalyanpur and James Fan 1 Andreas Thor 0ed5bf33ce4bf0c34dc7f2846b17ac4c28e3d519 University of Leipzig Thor Andreas Andreas Thor 3 Guillermo Palma ec8c21a30b57df2d5a8b55e8e3a0723ec3446bd0 Universidad Simon Bolivar Palma Guillermo Guillermo Palma 2 Cynthia Chang 7aff73c97fdc96b65af1059d4c977fc59eb71e36 RPI Chang Cynthia Cynthia Chang Vinay Chaudhri f0bbfc648c07c071dbea8609a6db246864cc225c SRI International Chaudhri Vinay Vinay Chaudhri ae1e85c676d68fdca4124f2316c1c800edde6c2f Thetida Zetta Zetta Aristotle University of Thessaloniki;Cardiff University Thetida Zetta Thetida Nigel Shadbolt University of Southampton Nigel Shadbolt, Professor of Artificial Intelligence and Head of the Web and Internet Science Group at the University of Southampton has been confirmed as the dinner speaker. He is a Director of the Web Science Trust, and of the Web Foundation - both organisations have a common commitment to advance our understanding of the Web and promote the Web‘s positive impact on society. In June 2009 together with Sir Tim Berners-Lee he was appointed an Information Advisor by the Prime Minister to help transform public access to Government information. A major output of this work has been the widely acclaimed data.gov.uk site - a single point of access for all Government non-personal public data. In May 2010 he was appointed by the Coalition Government to the Public Sector Transparency Board responsible for setting open data standards across the public sector and developing the legal Right to Data. He also chairs the Local Data Panel seeking to promote and develop open data approaches within Local Government. Shadbolt Nigel Nigel Shadbolt Lorenz Bühmann af15b119f30b750e47a5b8658ee5dee630219263 Lorenz Bühmann University of Leipzig Lorenz Bühmann Spiros Skiadopoulos University of Peloponnese Skiadopoulos Spiros Spiros Skiadopoulos George Mason University 9ee90784167a09162a38823d4627a53700513d3f Paulo Costa Paulo Costa Paulo Costa Hima Yalamanchili 944321034d9be8f579a352fd674e6a9354bfda62 Kno.e.sis Center, Wright State University Yalamanchili Hima Hima Yalamanchili 2012-11-13T16:00:00+05:00 2012-11-13T15:30:00+05:00 Coffee Break Hanmin Jung 5c0260fcfaaaaa4144abe3272262b847c660f0c2 Korea Institute of Science and Technology Information Jung Hanmin Hanmin Jung Julien Cojan 495815152a22a8af9ddcb916ad8986de667a76e7 INRIA Cojan Julien Julien Cojan Jacobs University Jacobs University 1 Beacon Hill Free University of Bozen-Bolzano Free University of Bozen-Bolzano TU Darmstadt TU Darmstadt Nitish Aggarwal 9b85e236d9f0aca98718221823309786cc2bc9d5 DERI, NUI Galway Aggarwal Nitish Nitish Aggarwal Universidad de Chile f341588c8c974d3b6fa4134e12700da871e8704f Gutierrez Claudio Gutierrez Claudio Gutierrez 998f9b4f01cf1bc5f74b60b15a755e4dcde30d76 Claudio University of Waikato University of Waikato 1 Stanford University Manuel Salvadores Manuel Salvadores e9278f3dd37dcb1f98d2f9184bab77d90228f858 Manuel Salvadores 92ea611cf55f95a0ffd94eca818bb9d8a3f9a735 Manolis Koubarakis Manolis Koubarakis Manolis National and Kapodistrian University of Athens Koubarakis Paul Alexander e4532edb374cb842d2bf282fc686bcb43b79833c Stanford University Alexander Paul Paul Alexander McNeill University of Edinburgh Fiona Fiona McNeill Fiona McNeill 38bbf1be818195da9679db35d8604b007404b542 CRIM CRIM Georgian University of Applied Sciences and Arts Northwestern Switzerland University of Applied Sciences and Arts Northwestern Switzerland University of Southern California University of Southern California Chris Baillie 6fb5a96a446e73369ffeff680a10a3988cdd3778 University of Aberdeen Baillie Chris Chris Baillie Data.gov, U.S. General Services Administration Jeanne Holm As the Evangelist for Data.Gov (an open government flagship project for the White House managed by GSA), Jeanne Holm leads collaboration and builds communities with the public, educators, developers, and international and state governments in using open government data. Jeanne is the Chief Knowledge Architect at NASA’s Jet Propulsion Laboratory, driving innovation through social media, virtual worlds, gaming, ontologies, and collaborative systems, including the award-winning NASA public portal (www.nasa.gov) and pioneering knowledge architectures within DoD. She is a Fellow of the United Nations International Academy of Astronautics and a Distinguished Instructor at UCLA, with more than 130 publications on information systems, knowledge management, and innovation. 5c8f5d03a924ab105d1dfd3cf1a7d0e66269f7f1 Jeanne Holm Jeanne Holm Paulo Pinheiro Da Silva University of Texas at El Paso Silva Paulo Paulo Pinheiro Da Silva PES Institute of Technology PES Institute of Technology Pavlos FORTH-ICS Pavlos Fafalios Fafalios e7d434b999fef7138cbe6610e4e6f0f787336c1b Pavlos Fafalios 2012-11-14T14:00:00+05:00 2012-11-14T12:30:00+05:00 Mentor Lunch Haase Peter Peter Haase Peter Haase fluid Operations fec12b76b11eceb8dce87a648d055583e575d0c6 Stephenson 90498c471a6ce3976f8c5413c586e5dab69a2a20 Martin Martin Stephenson Martin Stephenson IBM Research Ian Davis 89f6d4dd2fb06a92b4bba8827b52a06fe2babb76 self Davis Ian Ian Davis Sergio Tessaris Free University of Bozen-Bolzano Tessaris Sergio Sergio Tessaris Massimo Paolucci 399afe97d2817172bb4122357cc622fc558ccd48 DoCoMo Euro labs Paolucci Massimo Massimo Paolucci Karin Breitman 5e768fc4a7f130549b112247be2d54de8e8d1c5d EMC R&D Brazil Breitman Karin Karin Breitman University of Applied Sciences Western Switzerland University of Applied Sciences Western Switzerland semantic sensor networks sensor data ssn sensor web 5th International Workshop on Semantic Sensor Networks 2012-11-12T09:00:00+05:00 The workshop aims to provide an inter-disciplinary forum to explore and promote the technologies related to a combination of semantic web and sensor networking. Specifically, to develop an understanding of the ways semantic web technologies can contribute to the growth, application and deployment of large-scale sensor networks on the one hand, and the ways that sensor networks can contribute to the emerging semantic web, on the other. SWE sensor data semantic sensor networks sensor web 2012-11-12T17:30:00+05:00 SWE semantic sensor networks, ssn, sensor web, SWE, sensor data SSN ssn ISWC2012 Industry Chair Marco Luca Sbodio Marco Luca Sbodio 577eb4f75845c4576b906544c29f5b4a729fb465 IBM Research Sbodio Marco Luca ISWC2012 Sponsorship Chair Giovanni Semeraro University of Bari Giovanni Semeraro Giovanni Semeraro 800698093350bcbb94beb80799c9e84dc7e0e9c4 York Sure-Vetter York GESIS - Leibniz Institute for the Social Sciences; University of Koblenz and Landau 0969bcedb4a9a13e8399506967a744d2dd11ebda Sure-Vetter York Sure-Vetter Nicola 3c1395bd919491feee209479341d32552e97231c Nicola Fanizzi Nicola Fanizzi Fanizzi University of Bari Workshop Chair 3 2012-11-10T17:00:00+05:00 2012-11-10T09:00:00+05:00 The world is changing fast and so is Elsevier. To build and deploy future research tools we need to seriously collaborate in a linked and distributed environment. Building on earlier developer events such as Executable Paper Challenge, Life Sciences Challenge and Apps for Science, Elsevier and the 11th International Semantic Web Conference are co-hosting CodeForScience Boston, a competition for building a tool that best extends how scientific researchers search, process, integrate and share. The application concept formulation and submission will take place online, followed by a live coding day on Saturday, November 10th at MIT Stata Center in Cambridge, MA. Prizes will be awarded. Registration for CodeForScience Boston will open in early October. Code for Science Linked Datathon Ghent University Van Deursen 20ce3b0262f51732189cf73813162ecc5c98cab9 Davy Van Deursen Davy Van Deursen Davy Workshop Chair Centrum Wiskunde & Informatica (CWI) Centrum Wiskunde & Informatica (CWI) Opher Etzion 713e1a26f5e8e6d3feaf55a8da75fd7a4556b8b3 IBM Research Lab , Haifa, Israel Etzion Opher Opher Etzion Reinvent Technology Reinvent Technology Esko Nuutila 54ea9acbfd1a086718c0b100e91eef0621683f25 Aalto University Nuutila Esko Esko Nuutila Andreas 5660b83b50341a585b7b38ffeafe0163142338ba Andreas Zankl Andreas Zankl University of Queensland Zankl Yannis Kalfoglou self Kalfoglou Yannis Yannis Kalfoglou Jönköping University Karl Karl Hammar Karl Hammar Hammar 8b3d5c5936c7907471dcdda70f09ccff222498be Vinh Nguyen Kno.e.sis Center, Wright State University Nguyen Vinh Nguyen Vinh a59985628718c1893d88b887589ccfeec889f6d1 3roundstones 3roundstones Raúl García-Castro 907b89a2b177f2730c2d6ab5c855331fb434d497 Universidad Politecnica de Madrid García-Castro Raúl Raúl García-Castro Craig Knoblock 2c2715555efac793759255fe12d117541cf52a37 Information Sciences Institute, University of Southern California Knoblock Craig Knoblock Craig National Technical University of Athens National Technical University of Athens 2012-11-14T12:30:00+05:00 Industry Track I 2012-11-14T11:00:00+05:00 Pieterjan De Potter 95508860a6e76ad872c5bbe9c98ba681be97098d Ghent University De Potter Pieterjan Pieterjan De Potter 2012-11-14T17:30:00+05:00 2012-11-14T16:00:00+05:00 Streaming and Geospatial DBMSs Rafael S. Gonçalves 051af5c11e8f64bc668957e15b9a3ea541a386b2 University of Manchester Rafael S. Gonçalves Rafael Gonçalves Ontology Classification Ana Armas, Bernardo Cuenca Grau and Ian Horrocks MORe: Modular Combination of OWL Reasoners for Ontology Classification Modularity 76490001 Ontology Classification Modularity MORe: Modular Combination of OWL Reasoners for Ontology Classification Ontologies Automated reasoning 76490001 Ontologies Ontologies,Ontology Classification,Automated reasoning,Modularity,Semantic Web Semantic Web MORe: Modular Combination of OWL Reasoners for Ontology Classification Classification is a fundamental reasoning task in ontology design, and there is currently a wide range of reasoners highly optimised for classification of OWL 2 ontologies. There are also several reasoners that are complete for restricted fragments of OWL 2 , such as the OWL 2 EL profile. These reasoners are much more efficient than fully-fledged OWL 2 reasoners, but they are not complete for ontologies containing (even if just a few) axioms outside the relevant fragment. In this paper, we propose a novel classification technique that combines an OWL 2 reasoner and an efficient reasoner for a given fragment in such a way that the bulk of the workload is assigned to the latter. Reasoners are combined in a black-box modular manner, and the specifics of their implementation (and even of their reasoning technique) are irrelevant to our approach. Semantic Web Automated reasoning Venkat Krishnamurthy ac348f610320d103494dc36b09ca8f88a509e76c YarcData Krishnamurthy Venkat Venkat Krishnamurthy Raghava Mutharaju 86122fca1f282f8027766d9b2d2ab1b56a078cd1 Kno.e.sis Center, Wright State University Mutharaju Raghava Raghava Mutharaju Chito Jovellanos de997ccbe7342b078f3416db20446f0f64a47e00 forward look Jovellanos Chito Chito Jovellanos Yves Raimond c99a220cc2b8dacc4d4de0342a3909c73eb4e2be BBC Raimond Yves Yves Raimond Aristotle University of Thessaloniki Aristotle University of Thessaloniki events EventMedia Live: Exploring Events Connections in Real-Time to Enhance Content reconciliation semantic web events social services social services reconciliation media Houda Khrouf, Vuk Milicic and Raphaël Troncy EventMedia Live: Exploring Events Connections in Real-Time to Enhance Content EventMedia Live: Exploring Events Connections in Real-Time to Enhance Content media An ever increasing amount of event-centric knowledge is spread over multiple social services, either materialized as calendar of events or illustrated by media items shared by people. Crawling data and mining in real-time events connection between these heterogeneous services is a key challenge to enhance events views. In this paper, we present Event-Media, a web-based environment that exploits real-time connections to deliver rich content describing events associated with media, and interlined with the Linked Data cloud. EventMedia exploits semantic web technologies and provides user-friendly interface with the aim to meet the user needs: relive experiences based on media, and support decision making for attending upcoming events. semantic web events,media,semantic web,reconciliation,social services Centre for Research and Technology Hellas (CERTH) Centre for Research and Technology Hellas (CERTH) Lantzaki Christina Lantzaki Christina Christina Lantzaki c18e69eed0eee4083b2373a5ed279dd42f56cb49 FORTH-ICS Marcel Karnstedt cb3d8caf8c55c72f11ec748f9a19c51a9fc8c91f DERI, NUI Galway Karnstedt Marcel Marcel Karnstedt IBM Research Lab , Haifa, Israel IBM Research Lab , Haifa, Israel ISWC2012 Keynote Speaker Alexey Boyarsky 7d67056b41145374f54fb83c800ac865f0afef23 EPFL Boyarsky Alexey Alexey Boyarsky Universidad Simon Bolivar Universidad Simon Bolivar 2 Isabel Cruz University of Illinois at Chicago Cruz Isabel Isabel Cruz Nikolaou National and Kapodistrian University of Athens Charalampos d6aff7cc4a58c5d21d04ab22d4faa7bd2bdb00bf Charalampos Nikolaou Charalampos Nikolaou Interoperability Achieving Interoperability through Semantics-based Technologies: The Instant Messaging Case Universal Instant Messaging Achieving Interoperability through Semantics-based Technologies: The Instant Messaging Case Verification Achieving Interoperability through Semantics-based Technologies: The Instant Messaging Case Interoperability,Composition,Ontology,Verification,Mediation,Universal Instant Messaging Universal Instant Messaging 76500017 Mediation Composition Ontology Amel Bennaceur, Valerie Issarny, Romina Spalazzese and Shashank Tyagi Mediation Composition The success of pervasive computing depends on the ability to compose a multitude of networked applications dynamically in order to achieve user goals. However, applications from different providers are not able to interoperate due to incompatible interaction protocols or disparate data models. Instant messaging is a representative example of the current situation, where various competing applications keep emerging. To enforce interoperability at runtime and in a non-intrusive manner, mediators are used to perform the necessary translations and coordination between the heterogeneous applications. Nevertheless, the design of mediators requires considerable knowledge about each application as well as a substantial development effort. In this paper we present an approach based on ontology reasoning and model checking in order to generate correct-by-construction mediators automatically. We demonstrate the feasibility of our approach through a prototype tool and show that it synthesises mediators that achieve efficient interoperation of instant messaging applications. Verification Ontology Interoperability 76500017 University of Oxford Ian Ian Horrocks 3361a8a2f71036d7ca03076a41f4d8ae08c71e97 Horrocks Ian Horrocks 2012-11-15T14:00:00+05:00 2012-11-15T12:30:00+05:00 Lunch Instance matching 2012-11-14T16:00:00+05:00 2012-11-14T17:30:00+05:00 60ab9a4dc6ed26b245c84595ffb60545d953e801 TU Darmstadt Heiko Paulheim Heiko Paulheim Paulheim Heiko North Carolina State University North Carolina State University rule-based inferencing SPARQL rules RDF rules 76490273 complexity The lightweight ontology language OWL RL is used for reasoning with large amounts of data. To this end, the W3C standard provides a simple system of deduction rules, which operate directly on the RDF syntax of OWL. Several similar systems have been studied. However, these approaches are usually complete for instance retrieval only. This paper asks if and how such methods could also be used for computing entailed subclass relationships. Checking entailment for arbitrary OWL RL class subsumptions is co-NP-hard, but tractable rule-based reasoning is possible when restricting to subsumptions between atomic classes. Surprisingly, however, this cannot be achieved in any RDF-based rule system, i.e., the W3C calculus cannot be extended to compute all atomic class subsumptions. We identify syntactic restrictions to mitigate this problem, and propose a rule system that is sound and complete for many OWL RL ontologies. rule-based inferencing complexity The Not-So-Easy Task of Computing Class Subsumptions in OWL RL RDF rules The Not-So-Easy Task of Computing Class Subsumptions in OWL RL Markus Krötzsch SPARQL rules spotlight 76490273 The Not-So-Easy Task of Computing Class Subsumptions in OWL RL RDF rules,SPARQL rules,rule-based inferencing,complexity ISWC2012 Panellist To realize the Smart Cities vision, applications can leverage the large availability of open datasets related to urban environments. Those datasets need to be integrated, but it is often hard to automatically achieve a high-quality interlinkage. Human Computation approaches can be employed to solve such a task where machines are ineffective. We argue that in this case not only people's background knowledge is useful to solve the task, but also people's physical presence and direct experience can be successfully exploited. In this paper we present UrbanMatch, a Game with a Purpose for players in mobility aimed at validating links between points of interest and their photos; we discuss the design choices and we show the high throughput and accuracy achieved in the interlinking task. Linking Smart Cities Datasets with Human Computation - the case of UrbanMatch 76500033 Game with Purpose Mobility 76500033 Mobility Linking Smart Cities Datasets with Human Computation - the case of UrbanMatch Linking Smart Cities Datasets with Human Computation - the case of UrbanMatch Game with Purpose,Smart cities,Mobility Smart cities Irene Celino, Simone Contessa, Marta Corubolo, Daniele Dell'Aglio, Emanuele Della Valle, Stefano Fumeo and Thorsten Krueger Game with Purpose Smart cities Bhavani Thuraisingham b7461b7b01dfa88ddde27fac264fc1ee0ed9abdc University of Texas at Dallas Thuraisingham Bhavani Bhavani Thuraisingham Pallavi Karanth 424c83c37480fe076d17371f2e4009687fdae253 PES Institute of Technology Karanth Pallavi Pallavi Karanth Milan Stankovic Hypios Milan 5bcf5873f64d7a8e7edb3d6ee7a19af043a82e81 9827f6c41cba244ffa9c1ac3f20a4cc6dd578e4a Stankovic Milan Stankovic Jagannathan Srinivasan Jagannathan Jagannathan Srinivasan Srinivasan f3f12b445bccb179c3316d72820d4cd6e781cff0 ace4c6bb5c40723c227d7258cebc356a725e16a2 Oracle ISWC2012 General Chair Harth AIFB, Karlsruhe Institute of Technology Andreas Harth Andreas Andreas Harth 0604d144b935121a11d7495b2751e5d3176fc6fa Ghent University Ghent University With SEKI@home, which stands for Search for Embedded Knowledge Items, we propose a generic, browser extension-based approach for crowdsourcing the task of knowledge extraction from arbitrary Web pages. As people with the extension installed browse a targeted Web page, the extension sends extracted knowledge items according to the customizable extraction rules to a centralized, optionally publicly accessible triple store. Thereby, simply by browsing the Web as usual, participants in the knowledge extraction task can help make previously locked-in knowledge openly accessible, e.g., via the standard SPARQL protocol. We have implemented and made available a prototype browser extension, which, after customization and adaptation, can serve as the basis for future knowledge extraction tasks. knowledge extraction browser extension json-ld SEKI@home, a Generic Approach for Crowdsourcing Knowledge Extraction from Arbitrary Web Pages semantic lifting web scraping semantic lifting json-ld SEKI@home, a Generic Approach for Crowdsourcing Knowledge Extraction from Arbitrary Web Pages knowledge extraction,browser extension,seki@home,web scraping,json-ld,semantic lifting seki@home knowledge extraction SEKI@home, a Generic Approach for Crowdsourcing Knowledge Extraction from Arbitrary Web Pages Thomas Steiner and Stefan Mirea browser extension seki@home web scraping Riichiro Mizoguchi 36f7c5e541fbc21084f8bc4c85215922ab342097 Osaka University Mizoguchi Riichiro Riichiro Mizoguchi Elena Cabrio 346975d5d464d0d7523593d6bef915a11ff219c4 INRIA Cabrio Elena Elena Cabrio Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions Semantic Stream Applied Description Logics Freddy Lecue, Anika Schumann and Marco Luca Sbodio Diagnosis, or the method to connect causes to its effects, is an important reasoning task for obtaining insight on cities and reaching the concept of sustainable and smarter cities that is envisioned nowadays. This paper, focusing on transportation and its road traffic, presents how road traffic congestions can be detected and diagnosed in quasi real-time. We adapt pure Artificial Intelligence diagnosis techniques to fully exploit knowledge which is captured through relevant semantics-augmented stream and static data from various domains. Our prototype of semantic-aware diagnosis of road traffic congestions, experimented in Dublin Ireland, works efficiently with large, heterogeneous information sources and delivers value-added services to citizens and city managers in quasi real-time. Semantic cities Semantic integration Semantic integration Semantic cities Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions Semantic Stream Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions 76500113 76500113 Applied Description Logics Semantic Stream,Applied Description Logics,Semantic cities,Semantic integration Pennsylvania State University Pennsylvania State University 5ac8032d5f6012aa1775ea2f63e1676bafd5e80b W3C Herman Ivan Ivan Herman Ivan Herman Felix Sasaki Felix d261d1f6f8985467426d818be79cca445350921e Felix Sasaki DFKI Sasaki Semantic similarity-driven decision support in the skeletal dysplasia domain Semantic similarity-driven decision support in the skeletal dysplasia domain Ontologies Semantic similarity Semantic similarity Biomedical ontologies have become a mainstream topic in medical research. They represent important sources of evolved knowledge that may be automatically integrated in decision support methods. Grounding clinical and radiographic findings in concepts defined by a biomedical ontology, e.g., the Human Phenotype Ontology, enables us to compute semantic similarity between them. In this paper, we focus on using such similarity measures to predict disorders on undiagnosed patient cases in the bone dysplasia domain. Different methods for computing the semantic similarity have been implemented. All methods have been evaluated based on their support in achieving a higher prediction accuracy. The outcome of this research enables us to understand the feasibility of developing decision support methods based on ontology-driven semantic similarity in the skeletal dysplasia domain. 76500161 Decision support Ontologies Semantic similarity-driven decision support in the skeletal dysplasia domain Semantic similarity,Decision support,Ontologies 76500161 Decision support Razan Paul, Tudor Groza, Andreas Zankl and Jane Hunter 2012-11-14T14:00:00+05:00 2012-11-14T12:30:00+05:00 Lunch f1f7489808a1c44bbb8626c040a81260dcf55319 Nokia Services Ora Lassila Lassila Ora Lassila Ora Ondrej Svab-Zamazal fa5753d57260962506098825ba084e18a71f53f6 University of Economics, Prague Svab-Zamazal Ondrej Ondrej Svab-Zamazal Martin Serrano ce606e6d2a74d6686ac48b6804bd59172b0f1930 DERI, NUI Galway Serrano Martin Martin Serrano ISWC2012 Session Chair Evaluations and Experiments Track 2012-11-13T18:30:00+05:00 2012-11-13T17:30:00+05:00 Coffee Break Paul Groth Paul Groth 8f53bc4c411d120973ce1112da11af8ee6630854 VU Amsterdam Groth Paul Trond Aalberg 7f46d911cde461f8d17efb913a74d69739d5189d Norwegian University of Science and Technology Aalberg Trond Trond Aalberg Semantic Enrichment by Non-Experts: Usability of Manual Annotation Tools manual semantic annotation non-expert users 76490161 76490161 spotlight usability Semantic Enrichment by Non-Experts: Usability of Manual Annotation Tools usability Most of the semantic content available has been generated automatically by using annotation services for existing content. Automatic annotation is not of sufficient quality to enable focused search and retrieval: either too many or too few terms are semantically annotated. User-defined semantic enrichment allows for a more targeted approach. We developed a tool for semantic annotation of digital documents and conducted an end-user study to evaluate its acceptance by and usability for non-expert users. This paper presents the results of this user study and discusses the lessons learned about both the semantic enrichment process and our methodology of exposing non-experts to semantic enrichment. Semantic Enrichment by Non-Experts: Usability of Manual Annotation Tools non-expert users Annika Hinze, Ralf Heese, Markus Luczak-Rösch and Adrian Paschke manual semantic annotation manual semantic annotation,non-expert users,usability Technical University of Hamburg Technical University of Hamburg Brandeis University Brandeis University Michael Martin University of Leipzig Martin Michael Michael Martin Tomas Vitvar 5b1941ad0dfc4b89c13abe7a20d0e1f71a7ff96a Czech Technical University in Prague Vitvar Tomas Tomas Vitvar Jie Tang Tsinghua University Tang Jie Jie Tang Pignotti f38d4d85c208f258905be447b9779723dbb5a830 Edoardo Pignotti Edoardo Pignotti Edoardo University of Aberdeen 2012-11-13T14:00:00+05:00 2012-11-13T12:30:00+05:00 Lunch Epimorphics Dave Reynolds c496c063ea605640c8bd5ae7ab3adce9bbef360e Dave Dave Reynolds Reynolds Rolf Grütter ebfcf3b9138cbb5c13a3511f20f055b3cecaa63f Swiss Federal Institute for Forest, Snow and Landscape Research Grütter Rolf Rolf Grütter Royal Military College of Canada Royal Military College of Canada Houda Khrouf e39c392263ad57cf60842b95a317046560efd4d4 EURECOM Khrouf Houda Houda Khrouf Domain Ontologies Entity-quality structure Knowledge representation 76500081 Experiences with modeling composite phenotypes in the SKELETOME project Tudor Groza, Andreas Zankl and Jane Hunter Domain Ontologies Knowledge representation 76500081 Experiences with modeling composite phenotypes in the SKELETOME project Entity-quality structure Semantic annotation of patient data in the skeletal dysplasia domain (e.g., clinical summaries) is a challenging process due to the structural and lexical differences existing between the terms used to describe radiographic findings. In this paper we propose an ontology aimed at representing the intrinsic structure of such radiographic findings in a standard manner, in order to bridge the different lexical variations of the actual terms. Furthermore, we describe and evaluate an algorithm capable of mapping concepts of this ontology to exact or broader terms in the main phenotype ontology used in the bone dysplasia domain. Domain Ontologies,Knowledge representation,Entity-quality structure Experiences with modeling composite phenotypes in the SKELETOME project Joseph Benik af3fba8dce83f4e83cfea6e70336af0c387b466b University of Maryland Benik Joseph Joseph Benik Rafael S. Gonçalves, Bijan Parsia and Ulrike Sattler Semantic Diff Concept-Based Semantic Difference in Expressive Description Logics Concept-Based Semantic Difference in Expressive Description Logics OWL Ontologies,Semantic Diff,NCI Thesaurus,Description Logics Semantic Diff Concept-Based Semantic Difference in Expressive Description Logics NCI Thesaurus 76490097 OWL Ontologies Description Logics Detecting, much less understanding, the difference between two description logic based ontologies is challenging for ontology engineers due, in part, to the possibility of complex, non-local logic effects of axiom changes. First, it is often quite difficult to even determine which concepts have had their meaning altered by a change. Second, once a concept change is pinpointed, the problem of distinguishing whether the concept is directly or indirectly affected by a change has yet to be tackled. To address the first issue, various principled notions of ``semantic diff'' (based on deductive inseparability) have been proposed in the literature and shown to be computationally practical for the expressively restricted case of ELHr-terminologies. However, problems arise even for such limited logics as ALC: First, computation gets more difficult, becoming undecidable for logics such as SROIQ which underly the Web Ontology Language (OWL). Second, the presence of negation and disjunction make the standard semantic difference too sensitive to change: essentially, any logically effectual change always affects all terms in the ontology. In order to tackle these issues, we formulate the central notion of finding the minimal change set based on model inseparability, and present a method to differentiate changes which are specific to (thus directly affect) particular concept names. Subsequently we devise a series of computable approximations, and compare the variously approximated change sets over a series of versions of the NCI Thesaurus (NCIt). 76490097 Description Logics NCI Thesaurus OWL Ontologies Anastasia Analyti 4a5cdf250f517ba7c69479854aa08bec803e7a7f FORTH-ICS Analyti Anastasia Anastasia Analyti urban data city data city data linked data 76500145 content platform Vanessa Lopez, Spyros Kotoulas, Marco Luca Sbodio, Martin Stephenson, Aris Gkoulalas-Divanis and Pol Mac Aonghusa urban data 76500145 privacy QuerioCity: A Linked Data Platform for Urban Information Management linked data city data,content platform,linked data,urban data,privacy,stream data In this paper, we present QuerioCity, a platform to catalog, index and query highly heterogenous information coming from complex systems, such as cities. A series of challenges are identified: namely, the heterogeneity of the domain and the lack of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), and the sensitivity of the information. We propose an approach for incremental and continuous integration of static and streaming data, based on Semantic Web technologies. The proposed system is unique in the literature in terms of handling of multiple integrations of available data sets in combination with flexible provenance tracking, privacy protection and continuous integration of streams. We report on lessons learnt from building the first prototype for Dublin. stream data content platform privacy QuerioCity: A Linked Data Platform for Urban Information Management stream data QuerioCity: A Linked Data Platform for Urban Information Management ISWC2012 Session Chair Li Tian 9b9f00d8b9230fb6fcbc9a6c2f4b01e5f8a7bfee Shanghai Jiao Tong University Tian Li Li Tian University of Sheffield d7ee6f0673af6e031b1096ee5870da5e3fea4050 Elbedweihy Khadija Khadija Elbedweihy Khadija Elbedweihy Workshop Chair Yulan He Yulan He KMi, The Open University a25aba451a126e8c0524bb7aa32862cae59b6470 Yulan He b86f92857f5251e30a79eb958165f51871c72ac8 Jacco Van Ossenbruggen 55937258805a240393727f8e803f0c787135bb91 Centrum Wiskunde & Informatica (CWI) Van Ossenbruggen Jacco Jacco Van Ossenbruggen ISWC2012 Session Chair Graz University of Technology Graz University of Technology Denilson Barbosa 7a5509a7eb7ac9f4ddc981bbee622fe02de12438 University of Alberta Barbosa Denilson Denilson Barbosa Shenghui Wang Shenghui Wang Shenghui 229b7f6634fe989f466d6029739c24aca58dfba3 Wang OCLC Prasenjit Mitra Pennsylvania State University Mitra Prasenjit Prasenjit Mitra Rommel N. Rommel N. Carvalho 2baa083d5522da52321c52d9a57a93ae837bb277 Rommel N. Carvalho Carvalho George Mason University 1 Norman Heino f573c7cf859e0924ed074c08d0099f9bdd08211b University of Leipzig Heino Norman Norman Heino Benoit Christophe Alcatel-Lucent Bell Labs France Christophe Benoit Benoit Christophe Taxonomy of items Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy Collaborative filtering Recommendation over temporal dynamics Recommendation over temporal dynamics Collaborative filtering Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy Makoto Nakatsuji, Yasuhiro Fujiwara, Toshio Uchiyama and Hiroyuki Toda Time weight collaborative filtering 76490353 Taxonomy of items Tracking user interests over time is important for making accurate recommendations. However, the widely-used time-decay-based approach worsens the sparsity problem because it deemphasizes old item transactions. We introduce two ideas to solve the sparsity problem. First, we divide the users' transactions into epochs i.e. time periods, and identify epochs that are dominated by interests similar to the current interests of the active user. Thus, it can eliminate dissimilar transactions while making use of similar transactions that exist in prior epochs. Second, we use a taxonomy of items to model user item transactions in each epoch. This well captures the interests of users in each epoch even if there are few transactions. It suits the situations in which the items transacted by users dynamically change over time; the semantics behind classes do not change so often while individual items often appear and disappear. Fortunately, many taxonomies are now available on the web because of the spread of the Linked Open Data vision. We can now use those to understand dynamic user interests semantically. We evaluate our method using a dataset, a music listening history, extracted from users' tweets and one containing a restaurant visit history gathered from a gourmet guide site. The results show that our method predicts user interests much more accurately than the previous time-decay-based method. 76490353 Collaborative filtering,Taxonomy of items,Recommendation over temporal dynamics,Time weight collaborative filtering Time weight collaborative filtering 26a374ad4de937252c044f7dd45aa48ecbdb4f16 Steve Harris Garlik Steve Harris Harris Steve Purdue University Purdue University 2012-11-14T17:45:00+05:00 Town Hall Meeting 2012-11-14T18:45:00+05:00 Introduced at ISWC 2009 it has become a tradition at ISWC to come together in a town hall meeting to have conference participants share ideas on what they would like to see at the future ISWCs and to discuss what works and what does not work at the conference. Please join the members of the conference organizing committee in an informal discussion about all the new events that we added to the conference program this year and tell us what you would like to see in the future and what you liked and didn't like this year. Czech Technical University in Prague Czech Technical University in Prague Yutaka Matsuo University of Tokyo Matsuo Yutaka Yutaka Matsuo 2012-11-13T16:00:00+05:00 2012-11-13T17:30:00+05:00 Knowledge Discovery 1 Raymond Lloyd cf196394170671ee44ac163dea7c63f307a6e9d4 IBM Research Lloyd Raymond Raymond Lloyd MIT MIT Deborah L. McGuinness 292f7f25a21bd6c41c784397092317016dfc987d McGuinness RPI 8e2d61e147457c49ea020e33df12d9eb67ff1bbe Deborah L. McGuinness Deborah Harald Sack 4d582458728456ce41559e03c6b992bf68a8268d Hasso Plattner Institute Sack Harald Harald Sack Robert Engels Engels 608c13d67ab3e5fe3be33c7b9f443ffeba4d3883 Robert Engels Robert self Ontology Dynamics Visualization and Presenation of Evolving Knowledge Temporality in Knowledge Capturing EvoDyn 2012 continues the tradition of EvoDyn 2011 and the IWOD workshop series in being the core annual event to discuss advances in the broad area of ontology dynamics, and to track recent work directly or indirectly related to the problem of evolving knowledge. The workshop focuses on analysis of trends and change in formal descriptions, but also in associated raw sources of knowledge (scientific publications, unstructured or semi-structured Web content, traditional data stores, e-mail or on-line discussion threads, etc.). We are especially interested in research targeted on various states of knowledge evolution, such as (a) conflicts, (b) consolidation, (c) discovery, (d) paradigm shifts, and (e) breakthroughs. We would like to trigger a comprehensive and coherent approach to studying the process of knowledge evolution by bringing together researchers and practitioners from the following fields: (i) Data mining and knowledge discovery in dynamic resources; (ii) Ontology dynamics and versioning; (iii) Trend analysis (in multiple applications, including internet search, corpus evaluation, etc.); (iv) Natural Language Processing (evolution of terminology, language use, semantics); (v) Knowledge Representation (temporal ontologies, temporal logics, belief revision, etc.); (vi) Discourse Analysis and Philosophy of Science (the definition and understanding of what particular phases of the knowledge evolution are, and how can we delimit, identify or even trigger them). Knowledge Evolution Ontology Dynamics 2012-11-12T09:00:00+05:00 EvoDyn Knowledge Integration and Analysis over Time Knowledge Evolution 2012-11-12T17:30:00+05:00 2nd Joint Workshop on Knowledge Evolution and Ontology Dynamics Knowledge Integration and Analysis over Time Representation of and Reasoning on Evolving Knowledge Temporality in Knowledge Capturing Ontology Dynamics, Knowledge Evolution, Temporality in Knowledge Capturing, Knowledge Integration and Analysis over Time, Visualization and Presenation of Evolving Knowledge, Representation of and Reasoning on Evolving Knowledge Representation of and Reasoning on Evolving Knowledge Visualization and Presenation of Evolving Knowledge 3 2 Ontologies Linked Data BioPortal is a repository of biomedical ontologies - the largest such repository, with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other languages, as well as a large number of medical terminologies that the US National Library of Medicine distributes in its own proprietary format. We have published the RDF based serializations of all these ontologies and their metadata at sparql.bioontology.org. This dataset contains 203M triples, representing both content and metadata for the 300+ ontologies; and 9M mappings between terms. This endpoint can be queried with SPARQL which opens new usage scenarios for the biomedical domain. This paper presents lessons learned from having redesigned several applications that today use this SPARQL endpoint to consume ontological data. SPARQL Linked Data Manuel Salvadores, Matthew Horridge, Paul Alexander, Ray W. Fergerson, Mark A. Musen and Natasha F. Noy Ontologies,SPARQL,RDF,Biomedical,Linked Data 76500177 Using SPARQL to Query BioPortal Ontologies and Metadata RDF 76500177 RDF Ontologies Biomedical Using SPARQL to Query BioPortal Ontologies and Metadata Using SPARQL to Query BioPortal Ontologies and Metadata Biomedical SPARQL classificational interpretation of mappings 76490017 A Formal Semantics for Weighted Ontology Mappings 76490017 weighted mappings,degree of satisfiability of a mapping,classificational interpretation of mappings spotlight weighted mappings degree of satisfiability of a mapping Ontology mappings are often assigned a weight or confidence factor by matchers. Nonetheless, few semantic accounts have been given so far for such weights. This paper presents a formal semantics for weighted mappings between different ontologies. It is based on a classificational interpretation of mappings: if O1 and O2 are two ontologies used to classify a common set X , then mappings between O1 and O2 are interpreted to encode how elements of X classified in the concepts of O1 are re-classified in the concepts of O2, and weights are interpreted to measure how precise and complete re-classifications are. This semantics is justifiable by extensional practice of ontology matching. It is a conservative extension of a semantics of crisp mappings. The paper also includes properties that relate mapping entailment with description logic constructors. A Formal Semantics for Weighted Ontology Mappings Manuel Atencia, Alexander Borgida, Jérôme Euzenat, Chiara Ghidini and Luciano Serafini degree of satisfiability of a mapping classificational interpretation of mappings A Formal Semantics for Weighted Ontology Mappings weighted mappings Yong-Bin Kang 03592bb632c380a23e5b27855f7488fd2bd219d6 Monash University Kang Yong-Bin Yong-Bin Kang 2012-11-13T17:30:00+05:00 2012-11-13T17:00:00+05:00 schema.org update Pascal Hitzler d9d5e01de07e6f9a5e8b66c44c995c5ca8cc3b63 Pascal Hitzler Hitzler Pascal Kno.e.sis Center, Wright State University 47df72dfef38eb3cd02cb6259bb46bcb5f5c55f3 Whitier Posters and Demos spotlight Milan Dojchinovski, Jaroslav Kuchar, Tomas Vitvar and Maciej Zaremba Web APIs social network Web APIs service selection Personalised Graph-based Selection of Web APIs Web services social network Personalised Graph-based Selection of Web APIs Web services service selection Personalised Graph-based Selection of Web APIs 76490033 76490033 personalisation ranking Web APIs, Web services, personalisation, ranking, service selection, social network personalisation Modelling and understanding various contexts of users is important to enable personalised selection of Web APIs in directories such as Programmable Web. Currently, relationships between users and Web APIs are not clearly understood and utilized by existing selection approaches. In this paper, we present a semantic model of a Web API directory graph that captures relationships such as Web APIs, mashups, developers, and categories. We describe a novel configurable graph-based method for selection of Web APIs with personalised and temporal aspects. The method allows users to get more control over their preferences and recommended Web APIs while they can exploit information about their social links and preferences. We evaluate the method on a real-world dataset from ProgrammableWeb.com, and show that it provides more contextualised results than currently available popularity based rankings. ranking Arantza Illarramendi Basque Country University Illarramendi Arantza Arantza Illarramendi Antidot Antidot Peter Boncz Boncz Peter 5f5b73584c42f1fc4d222cb3633ac432b101a16a Peter Boncz da89467b6c6397e5e8ebac0f8c307fabc54664b4 Centrum Wiskunde & Informatica (CWI) Alberto Lavelli 05ca6f4ae400a2e99af6d881295feb059c9f6645 Fondazione Bruno Kessler Lavelli Alberto Alberto Lavelli Lehigh University Lehigh University Thomas Gottron 9c5c70601201113cdb69fc9026b89b5ea04067f0 University of Koblenz and Landau Gottron Thomas Thomas Gottron Industry Track IV 2012-11-14T17:30:00+05:00 2012-11-14T16:00:00+05:00 Bijan Parsia Bijan Parsia Bijan c9ef3edda6fd12cc1e9113217a75e5074e2ee80e Parsia University of Manchester Linköping University Eva Blomqvist 6ae81c4db26362dadb297719b4178e23dd011c6d Eva Blomqvist Eva f346d71f8a4b81034daeeb94d93d4eb7c0c8157f Blomqvist Carsten Keßler University of Münster Keßler Carsten Carsten Keßler Charles University in Prague Charles University in Prague Ontology Matching Discovering Concept Coverings in Ontologies of Linked Data Sources Discovering Concept Coverings in Ontologies of Linked Data Sources Ontology Matching Linked Data spotlight 76490417 Rahul Parundekar, Craig Knoblock and José Luis Ambite Despite the increase in the number of linked instances in the Linked Data Cloud in recent times, the absence of links at the concept level has resulted in heterogenous schemas, challenging the interoperability goal of the Semantic Web. In this paper, we address this problem by finding alignments between concepts from multiple Linked Data sources. Instead of only considering the existing concepts present in each ontology, we hypothesize new composite concepts defined as disjunctions of conjunctions of (RDF) types and value restrictions, which we call restriction classes, and generate alignments between these composite concepts. This extended concept language enables us to find more complete definitions and to even align sources that have rudimentary ontologies, such as those that are simple renderings of relational databases. Our concept alignment approach is based on analyzing the extensions of these concepts and their linked instances. Having explored the alignment of conjunctive concepts in our previous work, in this paper, we focus on concept coverings (disjunctions of restriction classes). We present an evaluation of this new algorithm to Geospatial, Biological Classification, and Genetics domains. The resulting alignments are useful for refining existing ontologies and determining the alignments between concepts in the ontologies, thus increasing the interoperability in the Linked Open Data Cloud. Discovering Concept Coverings in Ontologies of Linked Data Sources Schema Alignment Linked Data Schema Alignment Ontology Matching,Schema Alignment,Linked Data 76490417 2012-11-13T15:30:00+05:00 Queries 2012-11-13T14:00:00+05:00 Lockheed Martin Lockheed Martin Kouji Kozaki, Hiroko Kou, Yuki Yamagata, Takeshi Imai, Kazuhiko Ohe and Riichiro Mizoguchi Browsing Causal Chains in a Disease Ontology disease ontology ontology visualization causal chain Browsing Causal Chains in a Disease Ontology In order to realize sophisticated medical information systems, many medical ontologies have been developed. We proposed a definition of disease based on River Flow Model which captures a disease as a causal chain of clinical disorders. We also developed a disease ontology based on the model. It includes definitions of more than 6,000 diseases with 17,000 causal relationships. This demonstration summarizes the disease ontology and a browsing system for causal chains defined in it. Browsing Causal Chains in a Disease Ontology disease ontology causal chain disease ontology,causal chain,ontology visualization ontology visualization Umbrich Jürgen Umbrich b9514eba2ed2ae41a072765fe3ed3544de10974f DERI, NUI Galway Jürgen Jürgen Umbrich SAP Labs USA SAP Labs USA Policies In this paper we discuss our experience with the design, development and deployment of the ourSpaces Virtual Research Environment. ourSpaces makes use of Semantic Web technologies to create a platform to support multidisciplinary research groups. This paper introduces the main semantic components of the system: a framework to capture the provenance of the research process, a collection of services to create and visualise metadata and a policy reasoning service. We also describe different approaches to support interaction between users and metadata within the VRE. We discuss the lessons learnt during the deployment process with three case study groups. Finally, we present our conclusions and future directions for exploration in terms of developing ourSpaces further. Provenance,Virtual Research Environment,Policies,NLG Provenance ourSpaces - Design and Deployment of a Semantic Virtual Research Environment ourSpaces - Design and Deployment of a Semantic Virtual Research Environment NLG NLG Virtual Research Environment Peter Edwards, Edoardo Pignotti, Alan Eckhardt, Kapila Ponnamperuma, Chris Mellish and Thomas Bouttaz 76500049 Provenance ourSpaces - Design and Deployment of a Semantic Virtual Research Environment 76500049 Policies Virtual Research Environment Daniel Daniel Gerber 7716ea9608422625fa7ba043975f357ef856060f Daniel Gerber University of Leipzig Gerber Sapienza Università di Roma Sapienza Università di Roma 1 Aristotle University of Thessaloniki 930225eb9728dd814a0580a32d4104850ae6d0c5 Efstratios Kontopoulos Efstratios Kontopoulos Kontopoulos Efstratios Oleg Ruchayskiy ed43ece6fa6d690c8c0930d1bb787fe744c12cd1 CERN Ruchayskiy Oleg Oleg Ruchayskiy 2012-11-11T09:00:00+05:00 PSW Information and Data Management The Semantic Web is growing at an enormous pace. However, the development of semantic web software applications is not yet mainstream. Reasons for that include one (or more) of the following research issues: lack of integrated development environments (IDEs, such as Visual Studio and Eclipse), poor programming language support, lack of standard testbeds and/or benchmarks, inadequate training, and perhaps the need for curricula revision. Properly addressing these issues requires interdisciplinary skills, and the collaboration between academia and industry. The First Workshop on Programming the Semantic Web invites submissions that explore the gap between today’s semantic web challenges, particularly the ones related to dealing with large amounts of data, with the lack of adequate tools. We are looking for contributions that discuss, promote and further advance the programming facet of the semantic web, including the development of new languages, extension of existing ones, and the inclusion of semantic enabled capabilities into existing IDEs. Programming languages for the Semantic Web, Information and Data Management, Semantic Web Technology Stack Information and Data Management 2012-11-11T17:30:00+05:00 First Workshop on Programming the Semantic Web Semantic Web Technology Stack Programming languages for the Semantic Web Programming languages for the Semantic Web Semantic Web Technology Stack Bernie Innocenti d5ef8a624230622cd0cae57440693e6a9fcba519 Sugar Labs Innocenti Bernie Bernie Innocenti Mikko Rinne Rinne bcdbe37390fab64e570d73c4efbc4b9322097c9f Aalto University Mikko Rinne Mikko University of Hannover University of Hannover 1 Lehmann Jens Jens Lehmann 01fee219e665ecea3905f361517b2bd4a344975d University of Leipzig Jens Lehmann cdecab02f3fb1d3d9c79e1a8a8730bd11ef9f2d3 Timothy Lebo Lebo Timothy Lebo RPI Timothy Technion Technion 2012-11-14T11:00:00+05:00 Provenance and Verification 2012-11-14T12:30:00+05:00 2012-11-14T16:00:00+05:00 Industry Track III 2012-11-14T17:30:00+05:00 PC Member at ISWC2012(Research Track) Semantic Web Company GmbH Semantic Web Company GmbH Managing the life-cycle of Linked Data with the LOD2 Stack Linked Data The LOD2 Stack is an integrated distribution of aligned tools which support the whole life cycle of Linked Data from extraction, authoring/creation via enrichment, interlinking, fusing to maintenance. The LOD2 Stack comprises new and substantially extended existing tools from the LOD2 project partners and third parties. The stack is designed to be versatile; for all functionality we define clear interfaces, which enable the plugging in of alternative third-party implementations. The architecture of the LOD2 Stack is based on three pillars: (1) Software integration and deployment using the Debian packaging system. (2) Use of a central SPARQL endpoint and standardized vocabularies for knowledge base access and integration between the different tools of the LOD2 Stack. (3) Integration of the LOD2 Stack user interfaces based on REST enabled Web Applications. These three pillars comprise the methodological and technological framework for integrating the very heterogeneous LOD2 Stack components into a consistent framework. In this article we describe these pillars in more detail and give an overview of the individual LOD2 Stack components. The article also includes a description of a real-world usage scenario in the publishing domain. Linked Data Linked Data,application integration,Provenance Managing the life-cycle of Linked Data with the LOD2 Stack application integration Sören Auer, Lorenz Bühmann, Christian Dirschl, Orri Erling, Michael Hausenblas, Robert Isele, Jens Lehmann, Michael Martin, Pablo N. Mendes, Bert van Nuffelen, Claus Stadler, Sebastian Tramp and Hugh Williams 76500001 Managing the life-cycle of Linked Data with the LOD2 Stack application integration 76500001 Provenance Provenance KAIST KAIST Iowa State University Iowa State University Harokopio University of Athens Harokopio University of Athens Oak Ridge National Laboratory Oak Ridge National Laboratory Fouad Zablith 5f4ccbf95c8b46eca5bd1ef1dc1d6b39970d0554 KMi, The Open University Zablith Fouad Fouad Zablith NICT NICT Roger Hall UALR Hall Roger Roger Hall Charles National Library of Medicine bf554f67d7b26477e3bf5bf955d4caacdc54a2d0 Olivier Bodenreider Olivier Bodenreider Olivier Bodenreider Alasdair J. G. Gray University of Manchester Gray Alasdair Alasdair J. G. Gray Vanessa Lopez 5c3ac25297fd6033d663d292004b1e8d977ceb5f IBM Research Lopez Vanessa Lopez Vanessa Jamie Taylor dd485fcb6436ae2a96fe33a418ee96e3f5293be1 Metaweb Taylor Jamie Jamie Taylor Gianluca Demartini 8ec78d4c2f61e72bfc53da2988a051c97c9c2c7f Gianluca Demartini Gianluca Demartini University of Fribourg 2012-11-14T11:00:00+05:00 Industry Track II 2012-11-14T12:30:00+05:00 2012-11-14T11:00:00+05:00 2012-11-14T12:30:00+05:00 Alternative Knowledge Representation Approaches Holger Wache 612ab1009b9f9bb7c31f53982a6de846e572dfff University of Applied Sciences and Arts Northwestern Switzerland Wache Holger Holger Wache Qunzhi Zhou 7e8834512c5d648768dbdca54a012b245e85634b University of Southern California Zhou Qunzhi Qunzhi Zhou ISWC2012 11th International Semantic Web Conference Tania Tudorache Tudorache Stanford University Tania Tudorache 638d023ba44fb531a81881698239ebb410af4790 Tania PC Member at ISWC2012(Doctoral Consortium) University of Wuerzburg University of Wuerzburg Klaas Dellschaft eae2717871287ee074d3e89a2ea58c2ee0026b8b University of Koblenz-Landau Dellschaft Klaas Klaas Dellschaft Nuance Communications Nuance Communications Oktie Hassanzadeh 1d9481a1e0d0c956587cf8457e6fd8fda97effdb IBM Research Hassanzadeh Oktie Oktie Hassanzadeh 2 NER Creating Enriched YouTube Media Fragments With NERD Using Timed-Text Media fragment Yunjia Li, Giuseppe Rizzo and Raphaël Troncy Creating Enriched YouTube Media Fragments With NERD Using Timed-Text NER Media annotation This demo enables the automatic creation of semantically annotated YouTube media fragments. A video is first ingested in the Synote system and a new method enables to retrieve its associated subtitles or closed captions. Next, NERD is used to extract named entities from the transcripts which are then temporally aligned with the video. The entities are disambiguated in the LOD clound and a user interface enables to browse through the entities detected in a video or get more information. We evaluated our application with 60 videos from 3 YouTube channels. Creating Enriched YouTube Media Fragments With NERD Using Timed-Text Media fragment,Media annotation,NER Media annotation Media fragment Ying Zhang 398b8d7fa1a9c358bf21f3310c7255fd6a791129 Centrum Wiskunde & Informatica (CWI) Zhang Ying Ying Zhang James Michaelis 890d7402bc41cdab3ce5ce860a6b46bf9903d32c RPI Michaelis James James Michaelis 2012-11-14T17:30:00+05:00 2012-11-14T16:00:00+05:00 This session provides an open forum for participants to present a topic of their choosing. Each presenter is limited to one slide and two minutes time. Presenters must submit the title and one pdf slide to an email address announced in the conference's opening session. Limited presentation slots will be awarded on a first-come-first-serve basis. Lightning Talks University of Liverpool University of Liverpool ISWC2012 Session Chair University of Manchester University of Manchester Samsung Information Systems America Samsung Information Systems America SKOS A key objective of multidimensional dataset analysis is to reveal patterns of interest to analysts. However, multidimensional analysis has been observed to be dicult for analysts, due to the challenges of both presenting and navigating large datasets. This work explores how initial summarizations of multidimensional datasets can be generated for consuming parties (designed to reduce the number of data points which would need to be displayed) driven by summarization policies based on provided dataset values. Additionally, functionality for explaining the derivation of summarizations is being developed - in line with prior work on aiding analyst interactions with data processing systems. To help drive development of this work, as well as provide illustrative use cases, we are presently developing a dataset summarization generator as part of greater work being done in the Foresight and Understanding from Scientific Exposition (FUSE) program. Applying Multidimensional Navigation and Explanation in Semantic Dataset Summarization OLAP,SKOS,Provenance OLAP Provenance James Michaelis, Deborah L. McGuinness, Cynthia Chang, Joanne Luciano and Jim Hendler SKOS Applying Multidimensional Navigation and Explanation in Semantic Dataset Summarization OLAP Applying Multidimensional Navigation and Explanation in Semantic Dataset Summarization Provenance Linked Data 2012-11-13T11:00:00+05:00 2012-11-13T12:30:00+05:00 Birmingham City University Birmingham City University 2012-11-13T09:00:00+05:00 2012-11-13T08:30:00+05:00 Opening Ceremony Strabon: A Semantic Geospatial DBMS SPARQL geospatial information Strabon: A Semantic Geospatial DBMS Kostis Kyzirakos, Manos Karpathiotakis and Manolis Koubarakis SPARQL We present Strabon, a new RDF store that supports the state of the art semantic geospatial query languages stSPARQL and GeoSPARQL. To illustrate the expressive power offered by these query languages and their implementation in Strabon, we concentrate on the new version of the data model stRDF and the query language stSPARQL that we have developed ourselves. Like GeoSPARQL, these new versions use OGC standards to represent geometries where the original versions used linear constraints. We study the performance of Strabon experimentally and show that it scales to very large data volumes and performs, most of the times, better than all other geospatial RDF stores it has been compared with. geospatial information RDF RDF, SPARQL, geospatial information Strabon: A Semantic Geospatial DBMS 76490289 76490289 RDF RDF/OWL Semantic Web Semantic Web Success Story: Practical Integration of Semantic Web Technology and Linked Data Principles in the Architecture and Implementation of an Enterprise Product Semantic Web Success Story: Practical Integration of Semantic Web Technology and Linked Data Principles in the Architecture and Implementation of an Enterprise Product Semantic Web,RDF/OWL,Linked Data,Enterprise,Commercial Semantic Web Success Story: Practical Integration of Semantic Web Technology and Linked Data Principles in the Architecture and Implementation of an Enterprise Product Chris Chaulk Enterprise Commercial RDF/OWL Enterprise Commercial Linked Data Linked Data Semantic Web c5eb2a2d51e4a1308410f623bafd4f86c3120def Ioannis Vlahavas Ioannis Vlahavas Aristotle University of Thessaloniki Vlahavas Ioannis EMC R&D Brazil EMC R&D Brazil Web of Data Valeria Fionda, Claudio Gutierrez and Giuseppe Pirró Semantic Navigation Script Language Script Language Semantic Navigation on the Web with swget Web of Data Semantic Navigation Semantic navigation in the Web of Data is crucial to exploit the power of thousand of RDF data sources today available. We present swget, a tool that enables to perform selective navigation of distributed semantic data sources, triggering of actions over data encountered during the navigation, retrieval of data and extraction of relevant Web fragments. At the core of swget there is a powerful navigational language called NautiLOD with a concise syntax and a formal semantics. swget can be exploited to write declarative specifications of information on the Web in the form of scripts that can be shared, mixed, and reused. We describe the architecture of the swget tool and present both a standalone version and an online portal where users can create their intelligent agents, launch them and be notified when results are ready. Besides, swget also provide an appealing visualization tool to explore and make sense of results Semantic Navigation on the Web with swget Semantic Navigation on the Web with swget Semantic Navigation,Script Language,Web of Data Alcatel-Lucent Bell Labs France Alcatel-Lucent Bell Labs France Hu f7074d05b74deb43ec150671bb9b226578d20f2b Nanjing University Wei Wei Hu Wei Hu Lyndon Nixon fedfee89e4699ebbd40cfd1aa1c44a1fd0220d7d STI International Nixon Lyndon Lyndon Nixon 1 Norwegian University of Science and Technology Norwegian University of Science and Technology Oracle Zhe Wu Wu Zhe Wu Zhe f7c491cd2bf21a4fb49b3bcf3d1de571e97b34c7 3 ISWC2012 Poster and Demo Chair Testbeds proposed so far to evaluate, compare, and eventually improve SPARQL query federation systems have still some limitations. Some variables and configurations that may have an impact on the behavior of these systems (e.g., network latency, data partitioning and query properties) are not sufficiently defined; this affects the results and repeatability of independent evaluation studies, and hence the insights that can be obtained from them. In this paper we evaluate FedBench, the most comprehensive testbed up to now, and empirically probe the need of considering additional dimensions and variables. The evaluation has been conducted on three SPARQL query federation systems, and the analysis of these results has allowed to uncover properties of these systems that would normally be hidden with the original testbeds. Federated Semantic Data Query Processing Strategies SPARQL Endpoints Gabriela Montoya, Maria-Esther Vidal, Oscar Corcho, Edna Ruckhaus and Carlos Buil-Aranda Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough? 76500306 Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough? Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough? Benchmarking Federated Semantic Data Query Processing Strategies Benchmarking,Federated Semantic Data Query Processing Strategies,SPARQL Endpoints SPARQL Endpoints 76500306 Benchmarking Laurens Rietveld 43940ba2ec7ad17ad9221094b5983ceb934b798e VU Amsterdam Rietveld Laurens Laurens Rietveld Adrian Paschke c352377f5d48b16e5e29253acaee884ebce39d74 Free University of Berlin Paschke Adrian Adrian Paschke Konrad Höffner 4ffd792932126fe6ab4d187c84c65c0f9c665057 University of Leipzig Höffner Konrad Konrad Höffner ISWC2012 Session Chair John Breslin DERI, NUI Galway Breslin John John Breslin comparing RDF/S knowledge bases,mapping blank nodes,versioning and synchronization mapping blank nodes versioning and synchronization comparing RDF/S knowledge bases comparing RDF/S knowledge bases Blank Node Matching and RDF/S Comparison Functions Yannis Tzitzikas, Christina Lantzaki and Dimitris Zeginis mapping blank nodes versioning and synchronization 76490577 Blank Node Matching and RDF/S Comparison Functions In RDF, a blank node (or anonymous resource or bnode) is a node in an RDF graph which is not identified by a URI and is not a literal. Several RDF/S Knowledge Bases (KBs) rely heavily on blank nodes as they are convenient for representing complex attributes or resources whose identity is unknown but their attributes (either literals or associations with other resources) are known. In this paper we show how we can exploit blank nodes anonymity in order to reduce the delta (diff) size when comparing such KBs. The main idea of the proposed method is to build a mapping between the bnodes of the compared KBs for reducing the delta size. We prove that finding the optimal mapping is NP-Hard in the general case, and polynomial in case there are not directly connected bnodes. Subsequently we present various polynomial algorithms returning approximate solutions for the general case. For making the application of our method feasible also to large KBs we present a signature-based mapping algorithm with n logn complexity. Finally, we report experimental results over real and synthetic datasets that demonstrate significant reductions in the sizes of the computed deltas. For the proposed algorithms we also provide comparative results regarding delta reduction, equivalence detection and time efficiency. Blank Node Matching and RDF/S Comparison Functions 76490577 Siegfried DERI, NUI Galway 583cac1297018405882f186064c6d98cd127af70 Siegfried Handschuh Siegfried Handschuh Handschuh Lexington Universidad de Zaragoza Fernando Bobillo Bobillo Fernando Bobillo Fernando 9a2a51c421e2f974324e2006f134f0353ce9ad61 Stardog Linked Data Catalog Linked Data Linked Data,Data Catalog,Data integration Data Catalog Linked Data Stardog Linked Data Catalog Data integration Data integration Evren Sirin and Kendall Clark Data Catalog Stardog Linked Data Catalog Luciano Serafini 7fea00a39da1a3986831556109303fc904b9f935 Luciano Luciano Serafini Fondazione Bruno Kessler Serafini University of Milano-Bicocca University of Milano-Bicocca Domain Entity Annotation Domain Entity Annotation,Linked Open Data,Collective Annotation Collective Annotation Recently, with the ever-growing of textual medicine records, annotating domain entities has been regarded as an important task in the biomedical field. On the other hand, the process of interlinking open data sources is actively pursued within the Linking Open Data (LOD) project. The number of entities and the number of properties describing semantic relationships between entities within the linked data cloud are very large. In this paper, we propose a knowledge-incentive approach based on LOD for entity annotation in the biomedical field. With this approach, we implement MeDetect, a prototype system to solve the problems mentioned above. The experimental results verify the effectiveness and efficiency of our approach. MeDetect: Domain Entity Annotation in Biomedical References Using Linked Open Data Li Tian, Weinan Zhang, Haofen Wang, Chenyang Wu, Yuan Ni, Feng Cao and Yong Yu MeDetect: Domain Entity Annotation in Biomedical References Using Linked Open Data Domain Entity Annotation Linked Open Data MeDetect: Domain Entity Annotation in Biomedical References Using Linked Open Data Linked Open Data Collective Annotation Josh Hanna 25ac417526d190329bcaa2a1ad47c6fb405eca5d UAMS Hanna Josh Josh Hanna Nicola Guarino ISTC-CNR Guarino Nicola Nicola Guarino Nathan Wilson 6173d79946f322357698f37a35613df9b0ea5412 Marine Biological Laboratory Wilson Nathan Nathan Wilson Siemens AG Siemens AG Purdue University Jan Vitek 2e4c9e6c607e2995e126a3831a649d982e38b8f3 Jan Vitek Vitek Jan A graduate of University of Oxford, Tim Berners-Lee invented the World Wide Web, an internet-based hypermedia initiative for global information sharing while at CERN, the European Particle Physics Laboratory, in 1989. He wrote the first web client and server in 1990. His specifications of URIs, HTTP and HTML were refined as Web technology spread. He is the 3Com Founders Professor of Engineering in the School of Engineering with a joint appointment in the Department of Electrical Engineering and Computer Science at the Laboratory for Computer Science and Artificial Intelligence (CSAIL) at the Massachusetts Institute of Technology (MIT) where he also heads the Decentralized Information Group (DIG). He is also a Professor in the Electronics and Computer Science Department at the University of Southampton, UK. He is the Director of the World Wide Web Consortium (W3C), a Web standards organization founded in 1994 which develops interoperable technologies (specifications, guidelines, software, and tools) to lead the Web to its full potential. He was a Director of the Web Science Trust (WST) launched in 2009 to promote research and education in Web Science, the multidisciplinary study of humanity connected by technology. Tim is a Director of the World Wide Web Foundation, launched in 2009 to coordinate efforts to further the potential of the Web to benefit humanity. W3C Tim Tim Berners-Lee Berners-Lee Tim Berners-Lee Myunggwon Hwang 24d25e5f8456e57401071561b732f03219d69ff6 Korea Institute of Science and Technology Information Hwang Myunggwon Myunggwon Hwang 83f86224b1408a5b9824b769231ecc20244cc1ff Karl Aberer Karl Aberer EPFL Karl Aberer 2 PC Member at ISWC2012(Poster and Demo) Amel Bennaceur 028205a96123a1988acee594ff6124e01f7ef03b INRIA Bennaceur Amel Amel Bennaceur Cardiff University Cardiff University Alex Crow af030da6d69716804cc2943a3be21f36596501b5 UALR Crow Alex Alex Crow ISWC2012 Session Chair PC Member at ISWC2012 Workshop Chair sensor data streaming data SRBench: A Streaming RDF/SPARQL Benchmark benchmark SRBench: A Streaming RDF/SPARQL Benchmark We introduce SRBench, a general-purpose benchmark primarily designed for streaming RDF/SPARQL engines, completely based on real-world data sets from the Linked Open Data cloud. With the increasing problem of too much streaming data but not enough tools to gain knowledge from them, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for publishing, sharing, analysing and understanding streaming data. To help researchers and users comparing streaming RDF/SPARQL (strRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a strRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The data sets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a concise, yet comprehensive set of queries that cover the major aspects of strRS processing. Finally, our work is complemented with a functional evaluation on three representative strRS engines: SPARQLStream, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art. streaming data sensor data benchmark,streaming RDF/SPARQL,streaming data,sensor data 76490625 streaming RDF/SPARQL Ying Zhang, Minh-Duc Pham, Oscar Corcho and Jean Paul Calbimonte streaming RDF/SPARQL benchmark 76490625 SRBench: A Streaming RDF/SPARQL Benchmark sparql earth observation linked geospatial data rdf Real Time Fire Monitoring Using Semantic Web and Linked Data Technologies fire monitoring Real Time Fire Monitoring Using Semantic Web and Linked Data Technologies TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery and exploitation of knowledge that is hidden in them. In this demo paper we demonstrate a fire monitoring service that we have implemented in context of the project TELEIOS and explain how Semantic Web and Linked Data technologies allow the service to go beyond relevant services currently deployed in various Earth Observation data centers. fire monitoring Real Time Fire Monitoring Using Semantic Web and Linked Data Technologies semantic web linked geospatial data Kostis Kyzirakos, Manos Karpathiotakis, George Garbis, Charalampos Nikolaou, Konstantina Bereta, Michael Sioutis, Ioannis Papoutsis, Themistoklis Herekakis, Dimitrios Michail, Manolis Koubarakis and Charis Kontoes semantic web,linked geospatial data,rdf,sparql,earth observation,fire monitoring earth observation semantic web sparql rdf 2012-11-13T15:30:00+05:00 2012-11-13T14:00:00+05:00 User Interfaces and Personalization Weinan Zhang 9764be5858b2d4d80d06adb366635c4b9a379d47 Shanghai Jiao Tong University Zhang Weinan Weinan Zhang Van de Walle Ghent University 209af6a5da064a4a0f0cb89a336bfeb0ebcc196d Rik Van de Walle Rik Rik Van de Walle National and Kapodistrian University of Athens National and Kapodistrian University of Athens Ralf Möller 48cda81324bcef6390f2117b8d3557fd5165c5ff Technical University of Hamburg Möller Ralf Ralf Möller University of Sheffield Stuart Stuart Wrigley Wrigley Stuart Wrigley 4004916254d5dd83b00d8109bba8e7b3e6b8b422 7250d9213c1ecbe41fc0bfeab1648bb87464c3c8 Joaquim Gabarro Universitat Politecnica de Catalunya Gabarro Joaquim Joaquim Gabarro Workshop Chair Archana Venbakam 03eb40feca579ad0606e75b8e9ab9d040376219f YarcData Venbakam Archana Archana Venbakam Abir Qasem Bridgewater College Qasem Abir Abir Qasem Erik Mannens 0fb8b8ec11b797cb181f04f8c7534a39dd42812c Ghent University Mannens Erik Erik Mannens The Linked Data Visualization Model The Linked Data Visualization Model Visualization,Interaction,Semantic Web,Linked Data The potential of the semantic data available in the Web is enormous but in most cases it is very difficult for users to explore and use this data. Applying information visualization techniques to the Semantic Web helps users to easily explore large amounts of data and interact with them. We devise a formal Linked Data Visualization model (LDVM), which allows to dynamically connect data with visualizations. The Linked Data Visualization Model Linked Data Interaction Semantic Web Visualization Linked Data Interaction Visualization Josep Maria Brunetti Fernández, Sören Auer and Roberto Garcia Semantic Web Kelly Reynolds a47e5aefc4755f4f150b16741fb248eb1bde8daf Lehigh University Reynolds Kelly Kelly Reynolds 3 1 Xingjian Zhang a50f4191e3e209b66518184da03845c8a3a4a475 Lehigh University Zhang Xingjian Xingjian Zhang PUC-Rio PUC-Rio Evren 199129950aa1a391f5e9ca3fce00a2e4c17f678c Evren Sirin Clark & Parsia Evren Sirin 36150f32d9f306f19c88d0edbbcda7371d9d881d Sirin LIPN - UMR 7030 Universit Paris 13 - CNRS LIPN - UMR 7030 Universit Paris 13 - CNRS Patrick Patrick Rodler 870381a179fb4188d537171d1ffc51f34afe00f1 3251c04c1eb11ec29b744fd57506f627ed8c51fa Alpen-Adria Universität Patrick Rodler Rodler Thompson Bryan Bryan Thompson Bryan Thompson Systap Bryan Thompson is the co-founder and Chief Scientist of SYSTAP, LLC and the lead architect of the bigdata® database platform. He is a visionary and entrepreneur working on cutting edge efforts in web architecture, the semantic web, machine learning, natural language processing, artificial intelligence, cognitive modeling, and decision- support systems. He believes that web scale graph databases, GPU accelerated graph processing, and Web 2.0 authoring models will make it possible to capture metadata about the relationships between evidence and conclusions within and across communities and offer services and user experiences that encourage and facilitate collaboration across communities when their areas of expertise touch on shared concerns. Mr. Thompson is a National Merit Scholar. He has been recognized for his contributions under the Small Business Innovation Research (SBIR) program and by the Federal Semantic Interoperability Community of Practice (SICoP). He recently presented at the prestigious Schloss Dagstuhl Seminar on Semantic Data Management. He is a past member of the W3C Advisory Committee and participated the standardization efforts for SPARQL 1.0, Web Services Architecture, and XML Topic Maps. University of Sheffield University of Sheffield Russell Newman 846ee9dd77f1c1746725f5bd92ea56beac3d28fc University of Southampton Newman Russell Russell Newman Corlosquet Massachusetts General Hospital Stéphane Corlosquet Stéphane Corlosquet 2e080186a0f0d5d34fb46f01d76a4813b8137a76 Stéphane Miel Vander Sande 9da436ca83fe1f6c31b15af200be0fee8e29faca Ghent University Vander Sande Miel Miel Vander Sande heterogeneous ontology query translation Heterogeneity of ontologies on the web of data is very important problem. To solve this problem, there are a lot of researches about ontology mapping/alignment/matching. This paper shows an application called “SPARQLoid” that is using a query rewriting method to enable the users to query any SPARQL endpoint with the user’s own ontology even when their mappings are not reliable enough. Often ontology matching is very difficult problem and it sometimes produces mappings under a certain reliability. Based on the given reliability degrees on those mappings, SPARQLoid allows users to query data in the target SPARQL endpoints by using their own (or a specified certain) ontology under a control of sorting order based on their mapping reliability. SPARQLoid - a Querying System using Own Ontology and Ontology Mappings with Reliability SPARQL SPARQLoid - a Querying System using Own Ontology and Ontology Mappings with Reliability SPARQL,heterogeneous ontology,query translation heterogeneous ontology query translation SPARQLoid - a Querying System using Own Ontology and Ontology Mappings with Reliability SPARQL Takahisa Fujino and Naoki Fukuta Consuming Reasoning The quantity of published Linked Data is increasing dramatically. However, applications that consume Linked Data are not yet widespread. Current approaches lack methods for seamless integration of Linked Data from multiple sources, dynamic discovery of available data and data sources, provenance and information quality assessment, application development environments, and appropriate end user interfaces. Addressing these issues requires well-founded research, including the development and investigation of concepts that can be applied in systems which consume Linked Data from the Web. Following the success of the 1st and the 2nd International Workshop on Consuming Linked Data, we organize the third edition of this workshop in order to provide a platform for discussion and work on these open research problems. The main objective is to provide a venue for scientific discourse – including systematic analysis and rigorous evaluation – of concepts, algorithms and approaches for consuming Linked Data. Consuming Reasoning Linked Data Data Management COLD-2012 Linked Data Search Query Processing Data Management Search 2012-11-12T09:00:00+05:00 Query Processing 2012-11-12T17:30:00+05:00 Linked Data, Consuming, Data Management, Query Processing, Search, Reasoning Third International Workshop on Consuming Linked Data Machine Perception Semantic Sensor Web The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception - explanation and discrimination - and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale. Sensor Data 76490145 An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices Semantic Sensor Web Machine Perception Resource-Constrained Environments Machine Perception, Semantic Sensor Web, Sensor Data, Mobile Device, Resource-Constrained Environments Mobile Device An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices Sensor Data 76490145 Mobile Device Cory Henson, Krishnaprasad Thirunarayan and Amit Sheth Resource-Constrained Environments 2012-11-13T11:00:00+05:00 Description Logic 2012-11-13T12:30:00+05:00 Yasuhiro Fujiwara 487e2f0e0d87252bba38742b712f6a316a23edc8 NTT Fujiwara Yasuhiro Yasuhiro Fujiwara tracing manufacturing process Fairtrace - Tracing the textile industry manufacturing process business process business process tracing Fairtrace - Tracing the textile industry Fairtrace - Tracing the textile industry Bruno Alves and Michael Schumacher manufacturing process,tracing,business process Giusy Di Lorenzo d309a68d87c0f4cba5dffb937a40866e811f87eb IBM Research Di Lorenzo Giusy Giusy Di Lorenzo ISWC2012 Session Chair École des Mines de Saint-Étienne École des Mines de Saint-Étienne Mike Dean Dean Mike Dean 6006a914f7d645142fadf08d9a4e33aee98416d0 Mike Raytheon BBN Technologies Michael Uschold dec13295cf321695245e9fce5dc7648f0ecaf7b6 Reinvent Technology Uschold Michael Michael Uschold White Hill TasLab, Informatica Trentina SpA 6fc18ceccfe31ac7c9d76fea4e28a96b6fdd804e Pavel Shvaiko Pavel Shvaiko Pavel Shvaiko Universidad Autonoma de Madrid Universidad Autonoma de Madrid St. James DERI, NUI Galway DERI, NUI Galway Marieke Van Erp Marieke Van Erp VU Amsterdam f4e16d18528b83fd8b91b603583cbfd8d15f30f2 Marieke Van Erp Giulia Masotti cfefdd0c8db7aaf05f1aac06f72fa7575f7bca2c Sapienza Università di Roma Masotti Giulia Giulia Masotti Peter Mika Yahoo! Research c0d6551197a0295bfc604841a994d544e0091665 Mika Peter Peter Mika Olivier Curé Olivier Curé 33cd62b318bd504dde9eea1cdcf0095394e4ba40 Olivier Université Paris-Est LIGM Curé Yahoo! Research Silicon Valley Yahoo! Research Silicon Valley 76500454 Thomas Malone The Semantic Web and Collective Intelligence The original vision of the Semantic Web was to encode semantic content on the web in a form with which machines can reason. But in the last few years, we've seen many new Internet-based applications (such as Wikipedia, Linux, and prediction markets) where the key reasoning is done, not by machines, but by large groups of people. This talk will show how a relatively small set of design patterns can help understand a wide variety of these examples. Each design pattern is useful in different conditions, and the patterns can be combined in different ways to create different kinds of collective intelligence. Building on this foundation, the talk will consider how the Semantic Web might contribute to - and benefit from - these more human-intensive forms of collective intelligence. The Semantic Web and Collective Intelligence 76500454 The Semantic Web and Collective Intelligence Fact Validation,Information Retrieval,Information Extraction,RDF,DBpedia One of the main tasks when creating and maintaining knowledge bases is to validate facts and provide sources for them in order to ensure correctness and traceability of the provided knowledge. So far, this task is often addressed by human curators in a three-step process: issuing appropriate keyword queries for the statement to check using standard search engines, retrieving potentially relevant documents and screening those documents for relevant content. The drawbacks of this process are manifold. Most importantly, it is very time-consuming as the experts have to carry out several search processes and must often read several documents. In this article, we present DeFacto (Deep Fact Validation) - an algorithm for validating facts by finding trustworthy sources for it on the Web. DeFacto aims to provide an effective way of validating facts by supplying the user with relevant excerpts of webpages as well as useful additional information including a score for the confidence DeFacto has in the correctness of the input fact. RDF Fact Validation Jens Lehmann, Daniel Gerber, Mohamed Morsey and Axel-Cyrille Ngonga Ngomo DeFacto - Deep Fact Validation 76490305 DeFacto - Deep Fact Validation Information Extraction Information Retrieval Information Retrieval DBpedia DBpedia spotlight Fact Validation DeFacto - Deep Fact Validation 76490305 Information Extraction RDF Alpen-Adria Universität Alpen-Adria Universität Souripriya Das 718cc70f8db224021b37eccce82b26647ed7f935 Oracle Das Souripriya Souripriya Das IT-BHU IT-BHU ISWC2012 Session Chair Philippe Cudré-Mauroux, Jeff Heflin, Evren Sirin, Tania Tudorache, Jérôme Euzenat, Manfred Hauswirth, Josiane Xavier Parreira, Jim Hendler, Guus Schreiber, Abraham Bernstein, Eva Blomqvist Proceedings of 11th International Semantic Web Conference (ISWC2012), November 11 - November 15, 2012 2012 Nov Proceedings of ISWC 2012 Mark Greaves 5bcf15a284a0794ebad572e885d9e856055c46e0 Vulcan Inc Greaves Mark Mark Greaves 2012-11-12T17:30:00+05:00 The ISWC 2012 Doctoral Consortium will take place as part of the 11th International Semantic Web Conference in Boston, US. This forum will provide PhD students an opportunity to share and develop their research ideas in a critical but supportive environment, get feedback from mentors who are senior members of the Semantic Web research community, explore issues related to academic and research careers, and build relationships with other Semantic Web PhD students from around the world. The Consortium aims to broaden the perspectives and improve the research and communication skills of these students as a way to contribute both to the individuals as well as to the broader research community. Doctoral Consortium 2012-11-12T09:00:00+05:00 Volker Haarslev Concordia University Haarslev Volker Volker Haarslev Berlin Institute of Technology Berlin Institute of Technology Sebastian Rudolph AIFB, Karlsruhe Institute of Technology Rudolph Sebastian Sebastian Rudolph Emily Merrill d0f1d1886458f69e26e77317ae79931f665a7fbc Massachusetts General Hospital Merrill Emily Emily Merrill University of Mannheim Heiner Heiner Stuckenschmidt bf40527b81390e320fabaea1845c7eb337f7f813 Stuckenschmidt Heiner Stuckenschmidt Universidad de Zaragoza Universidad de Zaragoza Create-Net Create-Net Anastasios Anastasios Kementsietsidis e6563946b9127806c6a94706e6181d5ff2eafc7e Anastasios Kementsietsidis Kementsietsidis IBM Research Semantic web Smart-Aleck: An Interestingness Algorithm for Large Semantic Datasets Smart-Aleck: An Interestingness Algorithm for Large Semantic Datasets Not every fact in a large semantic dataset is of interest to an application. In the Smart-Aleck project, we have designed and implemented an interestingness algorithm that filters facts and joins them to generate new facts with higher levels of interestingness. The algorithm defines different levels of interestingness based on the semantic operations involved in generating interesting facts. The application of the algorithm is a Web site that presents a new interesting fact, rendered in English, each time users visit or refresh the page. The facts are generated from an integration of over half a billion triples from large semantic datasets including YAGO, Dbpedia, DataHub and Timbl. The uniqueness of the Smart-Aleck algorithm lies in its ability not merely to select interesting facts from the datasets but to generate new facts by joining two or more facts, possibly from different sources, by applying several comparison, chaining, grouping, aggregation and quantification operations on RDF triples. The implementation of Smart-Aleck on the web site is useful to everyone on the net to satisfy their curiosity, acquire general knowledge and design quizzes. It also has business potential as a feed for “fact-of-the-day” applications on cell phones and tablets. Interestingness,Fact generation,Semantic web,Algorithm,Dataset Fact generation Kavi Mahesh and Pallavi Karanth Algorithm Fact generation Algorithm Smart-Aleck: An Interestingness Algorithm for Large Semantic Datasets Semantic web Interestingness Dataset Interestingness Dataset 76490481 An increasing amount of data is published and consumed on the Web according to the Linked Data paradigm. In consideration of both publishers and consumers, the temporal dimension of data is important. In this paper we investigate the characterisation and availability of temporal information in Linked Data at large scale. Based on an abstract definition of temporal information we conduct experiments to evaluate the availability of such information using the data from the 2011 Billion Triple Challenge (BTC) dataset. Focusing in particular on the representation of temporal meta-information, i.e., temporal information associated with RDF statements and graphs, we investigate the approaches proposed in the literature, performing both a quantitative and a qualitative analysis and proposing guidelines for data consumers and publishers. Our experiments show that the amount of temporal information available in the LOD cloud is still very small; several different models have been used on different datasets, with a prevalence of approaches based on the annotation of RDF documents. temporal information, temporal annotation, linked data temporal annotation temporal annotation temporal information 76490481 linked data temporal information On the Diversity and Availability of Temporal Information in Linked Open Data On the Diversity and Availability of Temporal Information in Linked Open Data linked data Anisa Rula, Palmonari Matteo, Andreas Harth, Steffen Stadtmüller and Andrea Maurino On the Diversity and Availability of Temporal Information in Linked Open Data google knowledge graph knowledge graph chrome extensions google Thomas Steiner, Ruben Verborgh, Raphaël Troncy, Joaquim Gabarro and Rik Van de Walle google knowledge graph Adding Realtime Coverage to the Google Knowledge Graph Adding Realtime Coverage to the Google Knowledge Graph In May 2012, the Web search engine Google has introduced the so-called Knowledge Graph, a graph that understands real-world entities and their relationships to one another. Entities covered by the Knowledge Graph include landmarks, celebrities, cities, sports teams, buildings, movies, celestial objects, works of art, and more. The graph enhances Google search in three main ways: by disambiguation of search queries, by search log-based summarization of key facts, and by explorative search suggestions. With this paper, we suggest a fourth way of enhancing Web search: through the addition of realtime coverage of what people say about real-world entities on social networks. We report on a browser extension that seamlessly adds relevant microposts from the social networking sites Google+, Facebook, and Twitter in form of a panel to Knowledge Graph entities. In a true Linked Data fashion, we interlink detected concepts in microposts with Freebase entities, and evaluate our approach for both relevancy and usefulness. The extension is freely available, we invite the reader to reconstruct the examples of this paper to see how realtime opinions may have changed since time of writing. google knowledge graph,knowledge graph,social networks,chrome extensions,google knowledge graph google chrome extensions Adding Realtime Coverage to the Google Knowledge Graph social networks social networks Hong Kong University of Science and Technology Hong Kong University of Science and Technology Sebastian Walter 90660a9e6019c34387bd44962645df91bb829f9d Bielefeld University Walter Sebastian Sebastian Walter University of Southampton dbac203aa2b30d8966a8988ead8eaaae73ea924f Mike Wald Wald Mike Mike Wald SPARQL 76490609 Why-provenance Provenance for SPARQL queries m-semirings Provenance for SPARQL queries Provenance for SPARQL queries Why-provenance,SPARQL,m-semirings,Difference Carlos Viegas Damásio, Anastasia Analyti and Grigoris Antoniou SPARQL Difference Difference m-semirings Why-provenance 76490609 Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models. 1 Harokopio University of Athens fa896e051199b582f635ce6817c3f93d75365d55 Dimitrios Dimitrios Michail Dimitrios Michail Michail Victor de Boer a56410ed7ee59d7010ab04961a0f9409fa78fa89 VU Amsterdam de Boer Victor Victor de Boer Roberto Garcia f392e3ebd35b26a8964e824ebd8324623bfb57fb Universitat de Lleida Garcia Roberto Roberto Garcia VU Amsterdam VU Amsterdam Statler University of Bremen University of Bremen Thomas Bosch d0d8009527476b79beb8f8736933263cac907aa1 GESIS - Leibniz Institute for the Social Sciences Bosch Thomas Thomas Bosch Direct Mapping In this demo we introduce Quest, a new system that provides SPARQL query answering with support for OWL~2~QL and RDFS entailments. Quest allows to link the vocabulary of an ontology to the content of a relational database through mapping axioms. These are then used together with the ontology to answer a SPARQL query by means of a single SQL query that is executed over the database. Quest uses highly-optimised query rewriting techniques to generate the SQL query which not only takes into account the entailments of the ontology and data, but is also 'lean' and simple so that it can be executed efficiently by any SQL engine. Quest supports commercial and open source databases, including database federation tools like Teiid to allow for Ontology Based Data Integration of relational and other sources (e.g., CSV, Excel, XML). Here we will briefly describe Quest mapping language, the query answering process and the most relevant optimisation techniques used by the system. We will conclude with a brief description of the content of this demo. Movie Ontology Direct Mapping R2RML SPARQL Movie Ontology SPARQL RDBMS RDF RDFS D2RQ OWL 2 RDB2RDF Quest: Efficient SPARQL-to-SQL for RDF and OWL SQL OWL 2 D2RQ RDB2RDF,R2RML,Direct Mapping,SPARQL,SQL,DBMS,RDBMS,RDF,RDFS,OWL 2,OWL 2 QL,IMDB,Movie Ontology,D2RQ RDFS RDBMS OWL 2 QL R2RML IMDB OWL 2 QL IMDB Quest: Efficient SPARQL-to-SQL for RDF and OWL DBMS Quest: Efficient SPARQL-to-SQL for RDF and OWL SQL Mariano Rodriguez-Muro, Josef Hardi and Diego Calvanese RDB2RDF DBMS RDF ISWC2012 Session Chair Aibo Tian 8e6e08bd78f610033fcb23b4004d6022fe158a1b University of Texas at Austin Tian Aibo Aibo Tian Philippe Cudré-Mauroux Philippe Cudré-Mauroux cd093dfee55f5ac86ec10c79157bff2ad1107ec9 Cudré-Mauroux Philippe University of Fribourg Tom 1dd0dab717be578c153b8ed70bee284845439706 Tom Heath Tom Heath Heath Talis Yue Ma 82ef74612bb2089432ad12fdaa528dc67857361c LIPN - UMR 7030 Universit Paris 13 - CNRS Ma Yue Yue Ma DERI, NUI Galway Giovanni Tummarello 90731dc3a8b2dfda68b0eeedfb1176c6bd494bfe Giovanni Tummarello Giovanni Tummarello 76490513 Microtask Microtask Ontology alignment Cristina Sarasua, Elena Simperl and Natasha F. Noy CrowdMAP: Crowdsourcing Ontology Alignment with Microtasks The last decade of research in ontology alignment has brought a variety of computational techniques to discover correspondences between ontologies. While the accuracy of automatic approaches has continuously improved, human contributions remain a key ingredient of the process: this input serves as a valuable source of domain knowledge that is used to train the algorithms and to validate and augment automatically computed alignments. In this paper, we introduce CROWDMAP, a model to acquire such human contributions via microtask crowdsourcing. For a given pair of ontologies, CROWDMAP translates the alignment problem into microtasks that address individual alignment questions, publishes the microtasks on an online labor market, and evaluates the quality of the results obtained from the crowd. We evaluated the current implementation of CROWDMAP in a series of experiments using ontologies and reference alignments from the Ontology Alignment Evaluation Initiative and the crowdsourcing platform CrowdFlower. The experiments clearly demonstrated that the overall approach is feasible, and can improve the accuracy of existing ontology alignment solutions in a fast, scalable, and cost-effective manner. 76490513 CrowdMAP: Crowdsourcing Ontology Alignment with Microtasks Crowdsourcing Ontology alignment,Microtask,Crowdsourcing CrowdMAP: Crowdsourcing Ontology Alignment with Microtasks Ontology alignment Crowdsourcing Christian Meilicke aa10dcc1abe225b12ac6c62c75224109957f8837 University of Mannheim Meilicke Christian Christian Meilicke ISWC2012 Session Chair REST biomass SPARQL linked data LEAPS: A Semantic Web and Linked data framework for the Algal Biomass Domain algae biomass ontologies LEAPS: A Semantic Web and Linked data framework for the Algal Biomass Domain REST SPARQL ontologies linked data algae LEAPS: A Semantic Web and Linked data framework for the Algal Biomass Domain Monika Solanki biomass,algae,linked data,ontologies,REST,SPARQL In this paper we present, LEAPS, a Semantic Web and Linked data framework for searching and visualising datasets from the domain of Algal biomass. LEAPS provides tailored interfaces to explore algal biomass datasets via REST services and a SPARQL endpoint for stakeholders in the domain of algal biomass. The rich suite of datasets include data about potential algal biomass cultivation sites, sources of CO2, the pipelines connecting the cultivation sites to the CO2 sources and a subset of the biological taxonomy of algae derived from the world's largest online information source on algae. Jinhyung Kim df4307372f71608e923e96708637b058e3f0dd7e Korea Institute of Science and Technology Information Kim Jinhyung Jinhyung Kim University of Modena and Reggio Emilia University of Modena and Reggio Emilia Anisa Rula b7618420d4c418a900f5f4cb4f8294f78fae2b47 University of Milano-Bicocca Rula Anisa Anisa Rula Linked Data Semantic Web Schema Matching Semantic Web Schema Matching Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing In this paper, we describe a mechanism for ontology alignment using instance based matching of types (or classes). Instance-based matching is known to be a useful technique for matching ontologies that have different names and different structures. A key problem in instance matching of types, however, is scaling the matching algorithm to (a) handle types with a large number of instances, and (b) efficiently match a large number of type pairs. We propose the use of state-of-the art locality-sensitive hashing (LSH) techniques to vastly improve the scalability of instance matching across multiple types. We show the feasibility of our approach with DBpedia and Freebase, two different type systems with hundreds and thousands of types, respectively. We describe how these techniques can be used to estimate containment or equivalence relations between two type systems, and we compare two different LSH techniques for computing instance similarity. Linked Data 76490048 Ontology Alignment, Schema Matching, Linked Data, Semantic Web Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing 76490048 Songyun Duan, Achille Fokoue, Oktie Hassanzadeh, Anastasios Kementsietsidis, Kavitha Srinivas and Michael Ward Ontology Alignment Ontology Alignment Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing University of Queensland University of Queensland 3 Tackling Climate Change:  Unfinished Business from the Last "Winter" Tackling Climate Change:  Unfinished Business from the Last "Winter" 76500456 76500456 In the 1990s, as the World Wide Web became not only world wide but also dense and ubiquitous, workers in the artificial intelligence community were drawn to the possibility that the Web could provide the foundation for a new kind of AI. Having survived the AI Winter of the 1980s, the opportunities that they saw in the largest, most interconnected computing platform imaginable were obviously compelling. With the subsequent success of the Semantic Web, however, our community seems to have stopped talking about many of the issues that researchers believe led to the AI Winter in the first place: the cognitive challenges in debugging and maintaining complex systems, the drift in the meanings ascribed to symbols, the situated nature of knowledge, the fundamental difficulty of creating robust models. These challenges are still with us; we cannot wish them away with appeals to the open-world assumption or to the law of large numbers. Embracing these challenges will allow us to expand the scope of our science and our practice, and will help to bring us closer to the ultimate vision of the Semantic Web. Tackling Climate Change:  Unfinished Business from the Last "Winter" Mark A. Musen Bert van Nuffelen 663f68e05ac0f7eb0997737bd0b6d54aea1d012f Tenforce van Nuffelen Bert Bert van Nuffelen DFKI DFKI W3C W3C Charis Kontoes 8c9f34c7ae4eaf63c5b0ba473eae819206215c9a National Observatory of Athens Kontoes Charis Charis Kontoes Hugh University of Southampton Glaser Hugh Glaser 623018b35a1850179ed2903e332d96978eeb1d4f Hugh Glaser Patel-Schneider Nuance Communications 35a838a13f014e0d3924f7a0aeeb929105fbf234 Peter Patel-Schneider Peter Peter Patel-Schneider ISWC2012 Session Chair Carlo Allocca 008147d42b7fef2e6877d91208558dc98b720422 KMi, The Open University Allocca Carlo Carlo Allocca Birte Glimm University of Ulm Birte Glimm Birte Glimm d9e3004543dab6b7586ec0c3846985b999320232 David De Roure University of Oxford Roure David David De Roure Sebastian Krause c47cf68971d68146ed0b1f7f382926f287fd8de5 DFKI Krause Sebastian Sebastian Krause Raytheon BBN Technologies Raytheon BBN Technologies Amit 55747506d0fd11467524803c0f0d390d8b9e79ad Sheth Amit Sheth Kno.e.sis Center, Wright State University Amit Sheth c903202d3919813029e4dc56efbe0a2b2443074c L3S Research Center L3S Research Center 76490497 Sentiment analysis 76490497 Semantic Sentiment Analysis of Twitter Hassan Saif, Yulan He and Harith Alani Semantic Sentiment Analysis of Twitter Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. ''Apple product'') as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification. semantic concepts feature interpolation. Semantic Sentiment Analysis of Twitter Sentiment analysis Sentiment analysis, semantic concepts, feature interpolation. feature interpolation. semantic concepts Toshio Uchiyama 1001d9a99745d35b47886955f9009e2802134293 NTT Uchiyama Toshio Toshio Uchiyama Konstantina Bereta Bereta Konstantina Konstantina Bereta 6dc2e6c44ba12728237ec901f3f5190970a8dbf3 National and Kapodistrian University of Athens 3 DEQA: Deep Web Extraction for Question Answering Question Answering DEQA: Deep Web Extraction for Question Answering Natural Language Processing Jens Lehmann, Tim Furche, Giovanni Grasso, Axel-Cyrille Ngonga Ngomo, Christian Schallhart, Andrew Sellers, Christina Unger, Lorenz Bühmann, Daniel Gerber, Konrad Höffner, David Liu and Sören Auer Web Extraction Despite decades of effort, intelligent object search remains elusive. Neither search engine nor semantic web technologies alone have managed to provide usable systems for simple questions such as "Find me a flat with a garden and more than two bedrooms near a supermarket." We introduce DEQA, a conceptual framework that achieves this elusive goal through combining state-of-the-art semantic technologies with effective data extraction. To that end, we apply DEQA to the UK real estate domain and show that it can answer a significant percentage of such questions correctly. DEQA achieves this by mapping natural language questions to SPARQL patterns. These patterns are then evaluated on an RDF database of current real estate offers. The offers are obtained using OXPATH, a state-of-the-art data extraction system, on the major agencies in the Oxford area and linked through LIMES to background knowledge such as the location of supermarkets. Linking Question Answering SPARQL Web Extraction Natural Language Processing Question Answering,Web Extraction,Linking,Conceptual Framework,Natural Language Processing,SPARQL DEQA: Deep Web Extraction for Question Answering Conceptual Framework 76500129 Conceptual Framework 76500129 SPARQL Linking Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures Linked Data Link Discovery,Linked Data,Reduction Ratio Reduction Ratio Axel-Cyrille Ngonga Ngomo Reduction Ratio Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures 76490369 Link Discovery Link Discovery Linked Data 76490369 Time-efficient algorithms are essential to address the complex linking tasks that arise when trying to discover links on the Web of Data. Although several lossless approaches have been developed for this exact purpose, they do not offer theoretical guarantees with respect to their performance. In this paper, we address this drawback by presenting the first Link Discovery approach with theoretical quality guarantees. In particular, we prove that given an achievable reduction ratio r, our Link Discovery approach HR3 can achieve a reduction ratio r'<=r in a metric space where distances are measured by the means of a Minkowski metric of any order p >= 2. We compare HR3 and the HYPPO algorithm implemented in LIMES 0.5 with respect to the number of comparisons they carry out. In addition, we compare our approach with the algorithms implemented in the state-of-the-art frameworks LIMES 0.5 and SILK 2.5 with respect to runtime. We show that HR3 outperforms these previous approaches with respect to runtime in each of our four experimental setups. Thanos G. Stavropoulos, Dimitris Vrakas and Ioannis Vlahavas Semantic Web,Web Services,Semantic Web Services,Tools Although the Semantic Web and Web Service technologies have already formed a synergy towards Semantic Web Services, their use remains limited. Potential adopters are usually discouraged by the plurality of methodologies and the lack of tools which in turn force them to acquire expert knowledge and commit to exhausting manual labor. This work proposes a novel, functional and user-friendly tool, named Iridescent, intended for both expert and non-expert users to rapidly create and edit Semantic Web Service descriptions, following the SAWSDL recommendation. The tool’s aim is twofold: to enable users manually create descriptions in a visual manner, providing a complete alternative to coding, and to semi-automate the process by matching elements and concepts and suggesting annotations. A state-of-the-art survey has been carried out to reveal critical requirements and compare Iridescent to existing tools. Usage scenarios demonstrate how Iridescent enhances the authoring process and in turn enables Intelligence e.g. in an Ambient Intelligence environment. Finally, the tool was methodically tested for usability and evaluated by a range of expert and non-expert users. Semantic Web Services Semantic Web Iridescent: a Tool for Rapid Semantic Web Service Descriptions Web Services Semantic Web Iridescent: a Tool for Rapid Semantic Web Service Descriptions Tools Tools Iridescent: a Tool for Rapid Semantic Web Service Descriptions Semantic Web Services Web Services Markus Luczak-Rösch 94a7f6b90cec414ecc1b1d92f07a4d0eac6da5a0 Free University of Berlin Luczak-Rösch Markus Markus Luczak-Rösch Andrea Maurino b169d9fa8bee3225df614c24b18db7a171010ee8 University of Milano-Bicocca Maurino Andrea Andrea Maurino RPI RPI Jeff Jeff Z. Pan University of Aberdeen e409b5eceff3b8cf4be69005301c6984fa6ceae3 Jeff Z. Pan Pan Stanford University Natasha Natasha F. Noy 7d8b34490f91ef43b84489a7630394b04a8c2c21 Natasha F. Noy Noy Matthias Nickles Matthias Matthias Nickles Technical University of Munich Nickles 9458c32cbaab2efbd347ca14440cbd68a9647114 KMi, The Open University KMi, The Open University c7ab3ec57f6237d69228d71e804b910f47eee0fb Ravindra Padmashree Ravindra Padmashree Ravindra Padmashree North Carolina State University Machine learning A Machine Learning Approach for Instance Matching Based on Similarity Metrics Machine learning Linking Open Data Shu Rong, Xing Niu, Evan Wei Xiang, Haofen Wang, Qiang Yang and Yong Yu Instance matching A Machine Learning Approach for Instance Matching Based on Similarity Metrics 76490449 Transfer learning A Machine Learning Approach for Instance Matching Based on Similarity Metrics Linking Open Data,Instance matching,Similarity matric,Machine learning,Transfer learning Similarity matric Linking Open Data Instance matching Transfer learning The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to the same real-world object. The problem of discovering owl:sameAs links between pairwise data sources is called instance matching. Most of the existing approaches addressing this problem rely on the quality of prior schema matching, which is not always good enough in the LOD scenario. In this paper, we propose a schema-independent instance-pair similarity metric based on several general descriptive features. We transform the instance matching problem to the binary classification problem and solve it by machine learning algorithms. Furthermore, we employ some transfer learning methods to utilize the existing owl:sameAs links in LOD to reduce the demand for labeled data. We carry out experiments on some datasets of OAEI2010. The results show that our method performs well on real-world LOD data and outperforms the participants of OAEI2010. Similarity matric 76490449 2 Driving Innovation with Open Data and Interoperability Driving Innovation with Open Data and Interoperability 76500455 76500455 Data.gov, a flagship open government project from the US government, opens and shares data to improve government efficiency and drive innovation. Sharing such data allows us to make rich comparisons that could never be made before and helps us to better understand the data and support decision making. The adoption of open linked data, vocabularies and ontologies, the work of the W3C, and semantic technologies is helping to drive Data.gov and US data forward. This session will help us to better understand the changing global landscape of data sharing and the role the semantic web is playing in it. This session highlights specific data sharing examples of solving mission problems from NASA, the White House, and many other governments agencies and citizen innovators. Jeanne Holm Driving Innovation with Open Data and Interoperability Garlik Garlik ISWC2012 Session Chair Rutgers University Rutgers University Auer 09ac456515dee0896e8eba4b06ae589bef2069cf Sören Auer Sören Auer Sören TU Chemnitz Alpen-Adria Universität Philipp Philipp Fleiss Fleiss b02bd2d523e7d1559b65f6f873c854ba9fa018c5 Philipp Fleiss Szymon Klarman 2e6a9d20cccc1f31cf01e0cf43929a60066ea900 VU Amsterdam Klarman Szymon Szymon Klarman Kathryn Dunn 44d9c6c8c5907c48fd929430f7b1c67bbdebb741 RPI Dunn Kathryn Kathryn Dunn Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids Semantic Web Semantic Web,complex event processing,smart grid 76500254 Semantic Web complex event processing 76500254 smart grid smart grid Semantic Web allows us to model and query time-invariant or slowly evolving knowledge using ontologies. Emerging applications in Cyber Physical Systems such as Smart Power Grids that require continuous information monitoring and integration present novel opportunities and challenges for Semantic Web technologies. Semantic Web is promising to model diverse Smart Grid domain knowledge for enhanced situation awareness and response by multi-disciplinary participants. However, current technology does pose a performance overhead for dynamic analysis of sensor measurements. In this paper, we combine semantic web and complex event processing for stream based semantic querying. We illustrate its adoption in the USC Campus Micro-Grid for detecting and enacting dynamic response strategies to peak power situations by diverse user roles. We also describe the semantic ontology and event query model that supports this. Further, we introduce and evaluate caching techniques to improve the response time for semantic event queries to meet our application needs and enable sustainable energy management. Qunzhi Zhou, Yogesh Simmhan and Viktor Prasanna complex event processing iExplore: Interactive Browsing and Exploring Biomedical Knowledge Semantic Web Semantic Web UMLS PubMed We present iExplore, a Semantic Web based application that helps biomedical researchers study and explore biomedical knowledge interactively. iExplore uses the Biomedical Knowledge Repository (BKR), which integrates knowledge from various sources ranging from information extracted from biomedical literature (from PubMed) to many structured vocabularies in the Unified Medical Language System (UMLS). The current version of BKR provides a unified provenance representation for 12 million semantic predications (triples with a predicate connecting a subject and an object) derived from 87 vocabulary families in the UMLS and 14 million predications extracted from 21 million PubMed abstracts. To engage the domain experts in studying and exploring such a comprehensive knowledge base, we developed the iExplore to: 1) visualize and navigate all the possible semantic predications related to concepts of interest, and 2) search for interesting links between concepts. We also provide an authorization mechanism for SPARQL queries generated by the iExplore to support licensed access to UMLS. We demonstrate the use of iExplore in two scenarios: 1) current research in biomedicine, and 2) re-exploration of two previously known literature-based discoveries. iExplore is available at http://knoesis.wright.edu/iExplore. semantic predication semantic predication semantic similarity RDF visualization knowledge exploration RDF visualization,UMLS,PubMed,knowledge exploration,semantic predication,semantic similarity,Semantic Web UMLS iExplore: Interactive Browsing and Exploring Biomedical Knowledge RDF visualization iExplore: Interactive Browsing and Exploring Biomedical Knowledge knowledge exploration semantic similarity PubMed Vinh Nguyen, Olivier Bodenreider, Jagannathan Srinivasan, Todd Minning, Thomas Rindflesch, Bastien Rance, Ramakanth Kavuluru, Hima Yalamanchili, Krishnaprasad Thirunarayan, Satya Sahoo and Amit Sheth Christian Schallhart f58131098ea4801c804a5537cb3d6968f68e7066 University of Oxford Schallhart Christian Christian Schallhart Cristina Sarasua 5ffe114aa7c2c48df85158f381783f55a382eb0d AIFB, Karlsruhe Institute of Technology Sarasua Cristina Cristina Sarasua linked API linked data Rapidly Integrating Services into the Linked Data Cloud service modeling linked API 76490545 service modeling Mohsen Taheriyan, Craig Knoblock, Pedro Szekely and José Luis Ambite linked data Rapidly Integrating Services into the Linked Data Cloud 76490545 linked data, linked API, service modeling The amount of data available in the Linked Data cloud continues to grow. Yet, few services consume and produce linked data. There is recent work that allows a user to define a linked service from an online service, which includes the specifications for consuming and producing linked data, but building such models is time consuming and requires specialized knowledge of RDF and SPARQL. This paper presents a new approach that allows domain experts to rapidly create semantic models of services by demonstration in an interactive web-based interface. First, the user provides examples of the service request URLs. Then, the system automatically proposes a service model the user can refine interactively. Finally, the system saves a service specification using a new expressive vocabulary that includes lowering and lifting rules. This approach empowers end users to rapidly model existing services and immediately use them to consume and produce linked data. Rapidly Integrating Services into the Linked Data Cloud Giovanni Grasso 1b061673dafcb1e36b8d5aebf4a79f9921f6be8a University of Oxford Grasso Giovanni Giovanni Grasso query processing Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig 76490241 Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig Spyros Kotoulas, Jacopo Urbani, Peter Boncz and Peter Mika Map-Reduce Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig SPARQL,query processing,Map-Reduce SPARQL We describe a system that incrementally translates SPARQL queries to Pig Latin and executes them on a Hadoop cluster. This system is designed to work efficiently on complex queries with many self-joins over huge datasets, avoiding job failures even in the case of joins with unexpected high-value skew. To be robust against cost estimation errors, our system interleaves query optimization with query execution, determining the next steps to take based on data samples and statistics gathered during the previous step. Furthermore, we have developed a novel skew-resistant join algorithm that replicates tuples corresponding to popular keys. We evaluate the effectiveness of our approach both on a synthetic benchmark known to generate complex queries (BSBM-BI) as well as on a Yahoo! case of data analysis using RDF data crawled from the web. Our results indicate that our system is indeed capable of processing huge datasets without pre-computed statistics while exhibiting good load-balancing properties. Map-Reduce SPARQL 76490241 query processing Clarendon Alexandra Poulovassilis Birkbeck College, University of London Poulovassilis Alexandra Alexandra Poulovassilis The Boston Park Plaza Hotel & Towers 617.426.2000 50 Park Plaza at Arlington Street, Boston, MA 02116 617.426.5545 Located in the heart of historic Back Bay, The Boston Park Plaza Hotel & Towers is one of Boston�s most recognized and renowned landmarks. The Boston Park Plaza, a member of Historic Hotels of America, was constructed in March, 1927 as part of the E.M. Statler Empire. With an unsurpassed Boston address, the hotel is located only 3 miles from Logan International Airport and only 200 yards from the nation�s first public parks, Boston Common & the Public Garden. The hotel is easily accessible to public transportation, world renowned shopping along Newbury Street, Faneuil Hall Marketplace, the Theatre & Financial Districts and most historic landmarks. Rich in history, The Boston Park Plaza Hotel & Towers has distinguished itself with classic elegance and personalized service that continues to attract travelers from all over the world who visit Boston for business, leisure or special events. Oracle Oracle 23bb759762da3a685f2657adb391386b270f004e Dimitris Dimitris Plexousakis University of Crete; FORTH-ICS Dimitris Plexousakis Plexousakis 3 Tom De Nies c96075b326e3cd7a080c63b70f0832522c65ac4c Ghent University De Nies Tom Tom De Nies YarcData YarcData 2012-11-12T14:00:00+05:00 Financial Information Management Using the Semantic Web Financial IM 2012-11-12T17:30:00+05:00 Recent financial crises have demonstrated the need for sophisticated modeling of and deep reasoning about financial events and associated financial information. These tasks present foundational and technical challenges which are best addressed using open standards developed by the Semantic Web community. The tutorial will explains foundational concepts in finance and semantic web technologies, present relevant standards and languages, and work through several use cases and motivating examples in detail. University of Mynuch University of Mynuch c0879a5783f8750335b2d2830dd7dbb99dc8f94b Grau University of Oxford Bernardo Cuenca Grau Bernardo Cuenca Grau Bernardo Dimitris Vrakas Aristotle University of Thessaloniki Dimitris Vrakas bc5d468ff719b251a08617bcad321a83a09757a4 Dimitris Vrakas bd1e3cb53855eb1dd72620052d91e85a7e483e1a Yunjia Li Yunjia Li Li University of Southampton Yunjia Avigdor Gal Technion Gal Avigdor Avigdor Gal Xingjian Zhang, Dezhao Song, Sambhawa Priya, Zachary Daniels, Kelly Reynolds and Jeff Heflin Exploring the Linked Data Cloud via Contextual Tag Cloud Linked Open Data,Tag Cloud Browsing,Billion Triples Challenge,Semantic Web Exploration Exploring the Linked Data Cloud via Contextual Tag Cloud Billion Triples Challenge Semantic Web Exploration Tag Cloud Browsing Exploring the Linked Data Cloud via Contextual Tag Cloud Billion Triples Challenge Semantic Web Exploration Tag Cloud Browsing Linked Open Data Linked Open Data We present the contextual tag cloud system, where the context defines a subset of instances, the tags are ontological terms (classes and properties), and the font sizes reflect the number of instances that use each tag. With our system, users can get familiar with the terms and understand how the dataset is populated; or they can dynamically add tags as context and investigate features or look at instances within the constrained subset. With a domain knowledge base, this system helps reveal the patterns of data. For the massive Linked Open Data cloud, it reveals the extent to which data are linked with regard to both the equivalent instances and ontology alignment. In order to achieve this function, we precompute the owl:sameAs closure, and support multiple levels of RDFS entailment. By using an inverted index and pruning algorithms, we design and implement a real time response system for the Billion Triple Challenge dataset. This submission is for the Billion Triples track in Semantic Web Challenge. Our system is available at http://gimli.cse.lehigh.edu:8080/btc/ ISWC2012 Session Chair Linked Data The distributed and heterogeneous nature of Linked Open Data requires flexible and federated techniques for query evaluation. In order to evaluate current federation querying approaches a general methodology for conducting benchmarks is mandatory. In this paper, we present a classification methodology for federated SPARQL queries. This methodology can be used by developers of federated querying approaches to compose a set of test benchmarks that cover diverse characteristics of different queries and allows for comparability. We further develop a heuristic called SPLODGE for automatic generation of benchmark queries that is based on this methodology and takes into account the number of sources to be queried and several complexity parameters. We evaluate the adequacy of our methodology and the query generation strategy by applying them on the 2011 billion triple challenge data set. Olaf Görlitz, Matthias Thimm and Steffen Staab Benchmark 76490113 RDF Linked Data Linked Data,SPARQL,RDF,Distributed Query Processing,Benchmark SPARQL SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data SPARQL 76490113 SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data Distributed Query Processing Distributed Query Processing RDF Benchmark Spyros Kotoulas Spyros Kotoulas f8f973f695ad43bf3e13d8f97ef19f4878dfcd9d Spyros Kotoulas e879e287903caecdd41354eb5ae7aff6d9bc741b IBM Research David Liu e41b33383ba7b27f60995dd9cc47e0f6e5c9e8ab University of Oxford Liu David David Liu Jay Banerjee 17a34dd13931213750711667cad1dce5bfe62592 Oracle Banerjee Jay Jay Banerjee Tryfonopoulos University of Peloponnese 3918b205494022409a1198958cc40ae1af4ccd84 Christos Tryfonopoulos Christos Tryfonopoulos Christos Carlos Buil-Aranda 05aa09c1188d0bdeacc24e7824731849b88fe11c Universidad Politecnica de Madrid Buil-Aranda Carlos Carlos Buil-Aranda Building Large Scale Relation KB from Text Relation Extraction Relation Extraction Recently more and more structured data in form of RDF triples have been published and integrated into Linked Open Data (LOD). While the current LOD contains hundreds of data sources with billions of triples, it has a small number of distinct relations compared with the large number of entities. On the other hand, Web pages are growing rapidly, which results in much larger number of textual contents to be exploited. With the popularity and wide adoption of open information extraction technology, extracting entities and relations among them from text at the Web scale is possible. In this paper, we present an approach to extract the subject individuals and the object counterparts for the relations from text and determine the most appropriate domain and range as well as the most confident dependency path patterns for the given relation based on the EM algorithm. As a preliminary results, we built a knowledge base for relations extracted from Chinese encyclopedias. The experimental results show the effectiveness of our approach to extract relations with reasonable domain, range and path pattern restrictions as well as high-quality triples. Building Large Scale Relation KB from Text Expectation Maximization Junfeng Pan, Haofen Wang and Yong Yu Linked Data Expectation Maximization Building Large Scale Relation KB from Text Linked Data,Relation Extraction,Expectation Maximization Linked Data William Hogan 16777bcdd23c216ab011065b29525a56f50679c9 UAMS Hogan William William Hogan 1 Mark A. Musen Mark A. Musen Stanford University Musen Mark Musen from Stanford‘s Center for Biomedical Informatics research has been confirmed as the second keynote speaker for ISWC 2012. Mark conducts research related to intelligent systems, the Semantic Web, reusable ontologies and knowledge representations, and biomedical decision support. His long-standing work on a system known as Protégé has led to an open-source technology now used by thousands of developers around the world to build intelligent computer systems and new computer applications for e-science and the Semantic Web. He is known for his research on the application of intelligent computer systems to assist health-care workers in guideline- directed therapy and in management of clinical trials. He is principal investigator of the National Center for Biomedical Ontology, one of the seven National Centers for Biomedical Computing supported by the Roadmap of the U.S. National Institutes of Health. Mark 60602b405f9d456ffc0e30d22d535100159b7874 Tomi Kauppinen University of Münster Kauppinen 08d31d978b13dc399e87221867af54d2b33c7830 Tomi Tomi Kauppinen Shilpa Arora 8e49167ae5c7dd76690d680c21ecf561b75de7a3 CMU Arora Shilpa Shilpa Arora 3 Get the Google Feeling: Supporting Users in Finding Relevant Sources of Linked Open Data at Web-Scale retrieval Thomas Gottron, Ansgar Scherp, Bastian Krayer and Arne Peters schema based LOD discovery schema index user support semantic search schema index retrieval,schema index,schema based LOD discovery,user support,semantic search schema based LOD discovery semantic search Searching for Linked Open Data (LOD) has yet not reached the easiness and comfort we are accustomed with when using document search engines such as Google. To get closer to this "Google feeling" when searching for LOD, we have developed LODatio. Our system supports various kinds of queries on LOD such as searching for LOD sources containing specific types, properties, sets of types and properties, as well as their relations. The LOD sources are ranked along with the number of instances found that match the query. In addition, LODatio provides Google-style features such as "Did you mean?" and "Related queries" that allow the users for refining and broadening their queries. Get the Google Feeling: Supporting Users in Finding Relevant Sources of Linked Open Data at Web-Scale Get the Google Feeling: Supporting Users in Finding Relevant Sources of Linked Open Data at Web-Scale retrieval user support Swiss Federal Institute for Forest, Snow and Landscape Research Swiss Federal Institute for Forest, Snow and Landscape Research Wells Fargo Bank Wells Fargo Bank University of Crete University of Crete provenance web 2012-11-12T12:30:00+05:00 provenance interchange metadata 2012-11-12T09:00:00+05:00 provenance, interchange , web, metadata metadata Getting to know PROV - the W3C Provenance Specifications provenance interchange Provenance (the origin or source) of information is critical in deciding whether information is to be trusted, how it should be integrated with other diverse information sources, and how to give credit to its originators when reusing it. In order to promote the widespread publication of provenance information on the Web, the W3C is producing the W3C PROV set of specifications. These specifications provide a basis for the common exchange of provenance information on the Web. This half- day tutorial provides you with an indepth dive into these specifications including handson information on how to publish, query and access provenance information. You will learn how to model your provenance data using the PROV data model and ontology, how to produce provenance information that enables integrity checking and inferences, as well as how to expose and acquire provenance information using PROV access mechanisms and services. web Emanuele Della Valle Emanuele Politecnico di Milano 1ee386235c89959195075bc4944d5c68f8265f96 Della Valle Emanuele Della Valle graph theory RDFS Reasoning on Massively Parallel Hardware Norman Heino and Jeff Z. Pan 76490129 computational geometry RDFS Reasoning on Massively Parallel Hardware computational geometry, graph theory, Hamilton cycles 76490129 RDFS Reasoning on Massively Parallel Hardware Hamilton cycles Recent developments in hardware have shown an increase in parallelism as opposed to clock rates. In order to fully exploit these new avenues of performance improvement, computationally expensive workloads have to be expressed in a way that allows for fine-grained parallelism. In this paper, we address the problem of describing RDFS entailment in such a way. Different from previous work on parallel RDFS reasoning, we assume a shared memory architecture. We analyze the problem of duplicates that naturally occur in RDFS reasoning and develop strategies towards its mitigation, exploiting all levels of our architecture. We implement and evaluate our approach on two real-world datasets and study its performance characteristics on different levels of parallelization. We conclude that RDFS entailment lends itself well to parallelization but can benefit even more from careful optimizations that take into account intricacies of modern parallel hardware. graph theory computational geometry Hamilton cycles Zachary Daniels b33acc5a64c946c76e5579fb0525a8a6bbd6cba5 Lehigh University Daniels Zachary Zachary Daniels ISWC2012 Session Chair University of Arizona University of Arizona Manos Karpathiotakis National and Kapodistrian University of Athens Manos Karpathiotakis Karpathiotakis Manos 9bf0e66d32e5828c334848d790c727298225d331 Hans Uszkoreit 288ac7498712bfe804d7abf3637e67947fa188e0 DFKI Uszkoreit Hans Hans Uszkoreit ISWC2012 Program Chair George Mason University George Mason University 2 Sequeda Juan F. Sequeda e36a6c5f10bf558670ec81424012f651b25e23a4 University of Texas at Austin Juan F. Sequeda Juan Adila A. Krisnadhi Kno.e.sis Center, Wright State University Krisnadhi Adila Adila A. Krisnadhi 1 Patrick Siehndel and Ricardo Kawase TwikiMe! Wikipedia TwikiMe! - User profiles that make sense. TwikiMe!,Twitter,Wikipedia,Semantic User Profile The use of social media has been rapidly increasing in the last years. Social media such as Twitter has become an important source of information for a variety of people. The public availability of data describing some of these social networks has led to a lot of research in this area. Link prediction, user classification and community detection are some of the main research areas related to social networks. In this paper we present a user modeling framework that uses Wikipedia as a frame to model user interests inside a social network. Our fine grained model of user interests reflects the areas a user is interested in as well as the level of expertise a user has in a certain field. Twitter TwikiMe! - User profiles that make sense. Twitter TwikiMe! Semantic User Profile Semantic User Profile TwikiMe! - User profiles that make sense. Wikipedia Lin Clark d853b032b7ac97ef3c2cbd135e5d3241933d835d Lin Clark Lin DERI, NUI Galway Clark Yannis Yannis Tzitzikas University of Crete; FORTH-ICS Tzitzikas Yannis Tzitzikas e9f061dc9064b1669786bbbd730b5e1a035b8e0d Stojanovic Ljiljana 504f4841078f792ee57902392352f07cee93449e Ljiljana Stojanovic FZI Research Center for Information Technology Ljiljana Stojanovic Machine Learning for NLP Text processing Semantic web literals Natural Language Processing (NLP) LL-NLP LL-NLP Tutorial: What to do with long literals? Ask the NLP community... Natural Language Processing (NLP), Named entities, Machine Learning for NLP, Semantic web literals, Text processing 2012-11-11T17:30:00+05:00 Named entities In this tutorial, we will start with the basics of natural language processing (NLP) and will explain different levels of analysis (segmentation, morphology, lexicon, syntax, semantic). We will then look at the specific tasks involved in mining sentences to extract resource description framework (RDF) triples and show the importance that each level of language analysis can play in such tasks. NLP researchers have been working on such tasks for many years and have developed many algorithms to perform part-of-speech tagging, parsing, semantic disambiguation, named entity recognition and relation extraction. We will present baseline algorithms for these tasks to show expected results from state-of-the-art research. As many algorithms use machine learning (ML) techniques, we will provide a simple introduction to the aims and general principles behind such techniques and explain why they are so important to the NLP community. We will also explore problems that arise when extracting knowledge from multiple sentences and how that is closely related to concept mapping problems, which are familiar to the semantic web community. Since fully automatic text mining is quite a difficult task, we will emphasize that semi-automatic approaches can be effective and viable solutions for facilitating the RDFization of textual data. Semantic web literals Machine Learning for NLP 2012-11-11T09:00:00+05:00 Text processing Natural Language Processing (NLP) Named entities 2 3 EURECOM EURECOM Alberto Musetti a5c3dd70656898635fee387e6d0d7443127225bd University of Bologna Musetti Alberto Alberto Musetti Berkley University of Fribourg University of Fribourg Kowledge Base Rewriting Hitting the Sweetspot: Economic Rewriting of Knowledge Bases Description Logics,Non-standard Reasoning,Kowledge Base Rewriting,Module Extraction Description Logics Three conflicting requirements arise in the context of knowledge base (KB) extraction: the size of the extracted KB, the size of the corresponding signature and the syntactic similarity of the extracted KB with the original one. Minimal module extraction and uniform interpolation assign an absolute priority to one of these requirements, thereby limiting the possibilities to influence the other two. We propose a novel technique for EL that does not require such an extreme prioritization. We propose a tractable rewriting approach and empirically compare the technique with existing approaches with encouraging results. Nadeschda Nikitina and Birte Glimm 76490385 Module Extraction Non-standard Reasoning Description Logics Kowledge Base Rewriting Hitting the Sweetspot: Economic Rewriting of Knowledge Bases Non-standard Reasoning Hitting the Sweetspot: Economic Rewriting of Knowledge Bases 76490385 Module Extraction a4ba6adf3f4b9a9497aba7a6695f1554960435d3 Pouchard Line Pouchard Line Line Pouchard Oak Ridge National Laboratory ISWC2012 Session Chair ISWC2012 Local Chair Federated and Stream Query Processing 2012-11-13T17:30:00+05:00 2012-11-13T16:00:00+05:00 Michael Schumacher 9508203bd1b9a2206c108aaf0672f654e17670a5 University of Applied Sciences Western Switzerland Schumacher Michael Michael Schumacher Seppo Törmä f3983b4836d4d9f5c3a2ca2c2d9dc30a06c2c718 Aalto University Törmä Seppo Seppo Törmä Rehab Albeladi c335243fcf4c7309810510cd86c03da1d4e67387 University of Southampton Albeladi Rehab Rehab Albeladi 3 This paper introduces a Linked Data application for automatically generating a story between two concepts in the Web of Data, based on formally described links. A path between two concepts is obtained by querying multiple linked open datasets; the path is then enriched with multimedia presentation material for each node in order to obtain a full multimedia presentation of the found path. Linked Data,Path retrieval,Multimedia Path retrieval Everything is Connected: Using Linked Data for Multimedia Narration of Connections between Concepts Path retrieval Everything is Connected: Using Linked Data for Multimedia Narration of Connections between Concepts Miel Vander Sande, Ruben Verborgh, Sam Coppens, Tom De Nies, Pedro Debevere, Laurens De Vocht, Pieterjan De Potter, Davy Van Deursen, Erik Mannens and Rik Van de Walle Linked Data Everything is Connected: Using Linked Data for Multimedia Narration of Connections between Concepts Linked Data Multimedia Multimedia INRIA & LIG INRIA & LIG University of Economics, Prague University of Economics, Prague Mirko Graziosi 51beb8b97e106eeaf5401d4313885d9dc079e611 Sapienza Università di Roma Graziosi Mirko Mirko Graziosi University of Maryland Baltimore County University of Maryland Baltimore County Christopher Matheus Bell Labs Research Matheus Christopher Christopher Matheus 1 2012-11-12T12:30:00+05:00 A Hands-on Introduction to the GoodRelations Ontology, Schema.org, RDFa and Microdata Authoring, Google Rich Snippets for Products, Yahoo, Bing, and Linked Open Commerce. The Good Relations ontology (http://purl.org/goodrelations/) is one huge success story of applying Semantic Web technology to business challenges. In this tutorial, we will (1) give a comprehensive overview and hands-on training on the conceptual structures of the GoodRelations ontology including patterns for ownership and demand, (2) present the full tool chain for producing and consuming GoodRelations- related data, (3) explain the long-term vision of linked open commerce, (4) describe the main challenges for future research in the field, and (5) discuss advanced topics, like access control, identity and authentication (e.g. with WebID); micropayment services (like Payswarm), and data management issues from the publisher and consumer perspective. WoD E-Commerce 2012-11-12T09:00:00+05:00 The Web of Data for E-Commerce in Brief ISWC2012 Session Chair Basque Country University Basque Country University FORTH-ICS FORTH-ICS University of Zurich University of Zurich Aidan Hogan, Emir Muñoz and Jürgen Umbrich entity search entity search linked data semantic web LODPeas: Like peas in a LOD (cloud) similarity similarity LODPeas: Like peas in a LOD (cloud) We present LODPeas: a system for browsing entities that are found to share many things in common in an RDF dataset. The system first offers standard keyword search to locate a focus entity. Once a focus entity has been found, other entities that share a lot in common with it are displayed in a graph-based visualisation. The degree to which two entities have a lot in common---their level of concurrence---is scored by looking at attributes (property--value pairs) that they share: attributes that are shared by few other entities are given higher weight, and additional shared attributes imply a stronger score. LODPeas is designed to scale for billions of triples and is built in an (almost) entirely domain-agnostic fashion, built on top of the RDF standards themselves and not requiring any domain-specific input. Herein, we describe the functionality of LODPeas, how the system was built over the BTC'12, and discuss possible applications. semantic web linked data linked data,semantic web,similarity,entity search LODPeas: Like peas in a LOD (cloud) Approximate Reasoning 76490081 Due to the high worst case complexity of the core reasoning problem for the expressive profiles of OWL 2, ontology engineers are often surprised and confused by the performance behaviour of reasoners on their ontologies. Even very experienced modellers with a sophisticated grasp of reasoning algorithms do not have a good mental model of reasoner performance behaviour. Seemingly innocuous changes to an OWL ontology can degrade classification time from instantaneous to too long to wait for. Similarly, switching reasoners (e.g., to take advantage of specific features) can result in wildly different classification times. In this paper we investigate performance variability phenomena in OWL ontologies, and present methods to identify subsets of an ontology which are performance-degrading for a given reasoner. When such (ideally small) subsets are removed from an ontology, and the remainder is much easier for the given reasoner to reason over, we designate them “hot spotsâ€?. The identification of these hot spots allows users to isolate difficult portions of the ontology in a principled and systematic way. Moreover, we devise and compare various methods for approximate reasoning and knowledge compilation based on hot spots. We verify our techniques with a select set of varyingly difficult ontologies from the NCBO BioPortal, and were able to, firstly, successfully identify performance hot spots against the major freely available DL reasoners, and, secondly, significantly improve classification time using approximate reasoning based on hot spots. Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies OWL Ontologies OWL Ontologies,Reasoner Performance Variability,Approximate Reasoning,NCBO BioPortal,Description Logics Description Logics NCBO BioPortal Reasoner Performance Variability Description Logics OWL Ontologies NCBO BioPortal 76490081 Approximate Reasoning Reasoner Performance Variability Rafael S. Gonçalves, Bijan Parsia and Ulrike Sattler Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies Gabriela Montoya 715cf5f8a0415885ab8deef351c3422ec23deafa Universidad Simon Bolivar Montoya Gabriela Gabriela Montoya Politecnico di Milano Politecnico di Milano 2 583b2ab35d1cef69e21b25a7f36ec5a36e11d31d Fabien Gandon INRIA Fabien Gandon Fabien Gandon 2012-11-11T17:30:00+05:00 rules, ontologies, RIF, SPARQL, declarative logic programs, OWL ontologies OWL SPARQL declarative logic programs RIF rules SPARQL OWL 2012-11-11T14:00:00+05:00 Semantic Web Rules: Fundamentals, Applications, and Standards rules declarative logic programs SW Rules RIF ontologies The area of semantic rules is perhaps the most important frontier today for the Semantic Web‘s core technology and standards. Recent progress includes major initial industry standards from W3C and OMG, and fundamental advances in the underlying knowledge representation techniques in declarative logic programs, including most recently for efficient higher-order defaults with sound integration of first order logic ontologies (OWL). Recent progress also includes methods to use rules for, or with, more expressive OWL ontologies; increasing integration of rules with query /search in SPARQL and relational databases; substantive translations between heterogeneous types of commercial rule engines; development of open-source tools for inferencing and interoperability; performance benchmarking of rule systems; a wide range of emerging applications including in business, science, and trust; and accelerating industry investments / acquisitions in the technology including by integrated software companies such as Oracle, IBM, and Microsoft. This tutorial will provide a comprehensive and up-to-date introduction to these developments and to the fundamentals of the key technologies and outstanding research issues involved. It will explore example application scenarios, overall requirements and challenges, and touch upon business /social value and strategy considerations. University of Ulm University of Ulm Customer experience Customer experience Adoption of Semantic Web technologies Customer Adoption of Semantic Web Technologies - Sharing our Experience at Oracle Customer Adoption of Semantic Web Technologies - Sharing our Experience at Oracle Adoption of Semantic Web technologies Semantic Web applications Semantic Web applications Customer Adoption of Semantic Web Technologies - Sharing our Experience at Oracle Souripriya Das, Jagannathan Srinivasan, Matthew Perry and Seema Sundara Semantic Web applications,Adoption of Semantic Web technologies,Customer experience Romina Spalazzese 9789fbbabfadc2d45999ced58b46ea27cdf6bed3 University of L'Aquila Spalazzese Romina Romina Spalazzese National Observatory of Athens ccaf4efbf7ddd58fb405c68c0b67027dc49216bd Herekakis Themistoklis Themistoklis Herekakis Themistoklis Herekakis Yan Kang bf6dcda5b66de1b951d431cff0b721739758b08e University of Maryland Kang Yan Yan Kang Tutorial Organizer Tejaswini Pendurthi b167e98e97780f32f226c471d8db763078fc70e7 UALR Pendurthi Tejaswini Tejaswini Pendurthi 2012-11-13T11:00:00+05:00 Search, question answering and entity summarization 2012-11-13T12:30:00+05:00 reasoning classification A key issue in semantic reasoning is the computational complexity of inference tasks on expressive ontology languages such as OWL DL and OWL 2 DL. Theoretical works have established worst-case complexity results for reasoning tasks for these languages. However, hardness of reasoning about individual ontologies has not been adequately characterised. In this paper, we conduct a systematic study to tackle this problem using machine learning techniques, covering over 350 real-world ontologies and four state-of-the-art, widely-used OWL 2 reasoners. Our main contributions are two-fold. Firstly, we learn various classifiers that accurately predict classification time for an ontology based on its metric values. Secondly, we identify a number of metrics that can be used to effectively predict reasoning performance. Our prediction models have been shown to be highly effective, achieving an accuracy of over 80%. ontology,classification,reasoning,performance,prediction,metrics metrics metrics reasoning classification prediction 76490193 prediction ontology Predicting Reasoning Performance Using Ontology Metrics spotlight Yong-Bin Kang, Yuan-Fang Li and Shonali Krishnaswamy Predicting Reasoning Performance Using Ontology Metrics 76490193 performance performance ontology Predicting Reasoning Performance Using Ontology Metrics University of Münster University of Münster Revealing Trends and Insights in Online Hiring Market Using Linking Open Data Cloud: Active Hiring a Use Case Study Search Based Application,Mashup,Geotagging,Analytics Analytics Mashup Revealing Trends and Insights in Online Hiring Market Using Linking Open Data Cloud: Active Hiring a Use Case Study Search Based Application Search Based Application Geotagging Amar Djalil Mezaour, Julien Law-To, Robert Isele, Thomas Schandl and Gerd Zechmeister Geotagging In the recent months, the web community observed a significant increase in the growing trend towards open data usage. While this usage is common and spread for publishing and managing public data of governments and administrations, the business side is still lacking of major concrete use cases to demonstrate the benefit of open linked data in enterprise information management. In this paper, we present "Active Hiring" a search based application on the cloud providing analytics on on-line job posts. Active Hiring application uses services from the LOD cloud to disambiguate, geotag and interlink data entities acquired from on-line job boards web sites. Mashup Analytics Revealing Trends and Insights in Online Hiring Market Using Linking Open Data Cloud: Active Hiring a Use Case Study data fusion linked data,data fusion,data integration,data quality data integration linked data As part of LOD2 project and OpenData.cz initiative, we are developing an ODCleanStore framework enabling management of Linked Data. In this paper, we focus on the query-time data fusion in ODCleanStore, which provides data consumers with integrated views on Linked Data; the fused data (1) has solved conflicts according to the preferred conflict resolution policies and (2) is accompanied with provenance and quality scores, so that the consumers can judge the usefulness and trustworthiness of the data for their task at hand. Linked Data Fusion in ODCleanStore Framework Jan Michelfeit and TomᚠKnap linked data data integration data quality data fusion Linked Data Fusion in ODCleanStore Framework data quality Linked Data Fusion in ODCleanStore Framework 3 033fde5cee235c127687c07129f1fff99761d503 Satya Satya Sahoo Satya Sahoo Sahoo Case Western Reserve University Thomas Krennwallner 4fbf23f97eb1c36561a72379cea71095200fbd60 Vienna University of Technology Krennwallner Thomas Thomas Krennwallner Thanh Tran AIFB, Karlsruhe Institute of Technology Tran Thanh Thanh Tran Sabou Marta MODUL University Vienna Marta Sabou Marta Sabou 0d48e252f345b200071d6faea2f171ed494fd0c7 Shanghai Jiao Tong University Shanghai Jiao Tong University Bernhard Schandl Gnowsis.com Bernhard Schandl 079c9cf4971f05cbfee8e2bbdb5f9d9cfa777b83 Bernhard Schandl Derivo Derivo Austrian Institute of Technology Austrian Institute of Technology Drupal Stéphane Corlosquet, Sudeshna Das, Emily Merrill, Paolo Ciccarese and Tim Clark JSON-LD Drupal,biomedical,WebID,JSON-LD,RDFa,real-world deployment biomedical JSON-LD Drupal as a Semantic Web platform Drupal Drupal as a Semantic Web platform biomedical real-world deployment RDFa WebID RDFa WebID Drupal as a Semantic Web platform real-world deployment Tutorial Organizer 2012-11-13T14:00:00+05:00 Evaluation of reasoning with ontologies 2012-11-13T15:30:00+05:00 Tommaso Tommaso Di Noia Di Noia Tommaso Di Noia 521d0f3de6896cd4408128038bbe40547f790324 Technical University of Bari Linked Data Naimdjon Takhirov, Fabien Duchateau and Trond Aalberg An Evidence-based Verification Approach to Extract Entities and Relations for Knowledge Base Population 76490561 Knowledge Extraction 76490561 Machine Learning An Evidence-based Verification Approach to Extract Entities and Relations for Knowledge Base Population Knowledge Extraction This paper presents an approach to automatically extract entities and relationships from textual documents. The main goal is to populate a knowledge base that hosts this structured information about domain entities. The extracted entities and their expected relationships are verified using two evidence based techniques: classification and linking. This last process also enables the linking of our knowledge base to other sources which are part of the Linked Open Data cloud. We demonstrate the benefit of our approach through series of experiments with real-world datasets. Linked Data Linked Data, Knowledge Extraction, Machine Learning Machine Learning An Evidence-based Verification Approach to Extract Entities and Relations for Knowledge Base Population Hassan Saif cfd1b87509b48cac8cacda96266298dd3dee5e0b KMi, The Open University Saif Hassan Hassan Saif e3191d9363d7a24690b14c565b6ef48e6515625b Josep Maria Brunetti Fernández Josep Maria Brunetti Fernández Universitat de Lleida Brunetti Fernández Josep Maria Shashank Tyagi f98a48be121617bbe5eef104b1caac2f6e295128 IT-BHU Tyagi Shashank Shashank Tyagi Thorsten Liebig Thorsten Liebig b8b5d199eaeda0a2baddde8d10ad26cad85bc635 Liebig Derivo Thorsten This paper describes InSciTe Adaptive, which is a technology intelligence service developed by KISTI. InSciTe Adaptive supports not only technology analyzing and forecasting based on diverse types of information such as paper, patents, reports, ads, web resources and so on but also user adaptive and guiding service by intelligent recognition of user preference. InSciTe Adaptive includes 7 services focusing on technologies, products, organizations, and nations: (a) Technology Navigation, (b) Technology Trends, (c) Core Elementary Technology, (d) Convergence Technology, (e) Agents Levels, (f) Agents Partners, and (g) Technology Roadmap. These services were implemented by combining Semantic Web technologies with text mining technologies. User Intention Semantic Web,User Adaptive,Technology Trends Analysis,User Intention,Technology Prediction Semantic Web InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention Technology Trends Analysis User Intention Semantic Web User Adaptive Technology Prediction User Adaptive InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention Technology Prediction Technology Trends Analysis Jinhyung Kim, Myunggwon Hwang, Do-Heon Jeong, Sa-Kwang Song and Hanmin Jung InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention Machine learning has become increasingly important in the context of Linked Data as it is an enabling technology for many important tasks such as link prediction, information retrieval or group detection. The fundamental data structure of Linked Data is a graph. Graphs are also ubiquitous in many other fields of application, such as social networks, bioinformatics or the World Wide Web. Recently, tensor factorizations have emerged as a highly promising approach to machine learning on graph-structured data, showing both scalability and excellent results on benchmark data sets, while matching perfectly to the triple structure of RDF. This tutorial will provide an introduction to tensor factorizations and their applications for machine learning on graphs. By the means of concrete tasks such as link prediction we will discuss several factorization methods in-depth and also provide necessary theoretical background on tensors in general. Emphasis is put on tensor models that are of interest to Linked Data, which will include models that are able to factorize large-scale graphs with millions of entities and known facts or models that can handle the open-world assumption of Linked Data. Furthermore, we will discuss tensor models for temporal and sequential graph data, e.g. to analyze social networks over time. Machine Learning, Tensor, Graph, Linked Data Linked Data Graph Tensor Machine Learning 2012-11-11T14:00:00+05:00 Learning on Linked Data: Tensors and their Applications in Graph-Structured Domains Machine Learning TAG Graph 2012-11-11T17:30:00+05:00 Linked Data Tensor da50c1e462618c97bab0b228703c098fa2f61eca Enrico Daga Daga Enrico Daga KMi, The Open University;STLab, ISTC-CNR Enrico 2 Semantic Web Rule Bases Rule Editor RuleML Thetida Zetta, Efstratios Kontopoulos and Nick Bassiliades S2REd: A Semantic Web Rule Editor Ontologies Rule Editor Semantic Web S2REd: A Semantic Web Rule Editor S2REd: A Semantic Web Rule Editor A key factor in the further progress of the Semantic Web is the development and wide-spread usage of rule- and logic-based applications. However, there is an evident lack of software tools that can assist end-users in developing such applications. Consequently, users usually resort to more generic tools that offer support at a syntactic level, but prove inadequate in semantically supporting the user. This paper presents S2REd, a Semantic Web rule editor that introduces a supplementary layer of semantic assistance during rule base development. The tool offers semantic assistance via: (i) The Semantic Tag Mapping window that provides a meta-modeling facility for generating schemas over various rule language versions and, (ii) the Namespace Dialog window, for loading ontologies that serve as the underlying vocabulary for expressing rule atoms. S2REd assists in developing RuleML rule bases, but is equally suitable for any other XML-based syntax for representing rule sets. Ontologies RuleML Semantic Web,RuleML,Rule Bases,Rule Editor,Ontologies Rule Bases Esteban Zimanyi Université Libre de Bruxelles Zimanyi Esteban Esteban Zimanyi University of Turin University of Turin 1 Magnus White 4fa7b334e50b4b9ed26b3e89afc72f7fdcef2734 University of Southampton White Magnus Magnus White Guus Schreiber VU Amsterdam Schreiber Guus Guus Schreiber Griffith University Griffith University Raphaël 76b8645ac23d412d99c23dd95e0fbbe092d3f730 Raphaël Troncy Raphaël Troncy EURECOM Troncy Alessandro Bozzon da1df13f99af0ce33a046cfbf5cb3f038812a620 Politecnico di Milano Bozzon Alessandro Alessandro Bozzon MODUL University Vienna MODUL University Vienna data aggregation Linked Enterprise Data: leveraging the Semantic Web stack in a corporate IS environment enterprise data linked data Linked Enterprise Data: leveraging the Semantic Web stack in a corporate IS environment enterprise data Linked Enterprise Data: leveraging the Semantic Web stack in a corporate IS environment data meshing semantic web data meshing linked enterprise data semantic web data aggregation linked data Fabrice Lacroix linked data,semantic web,linked enterprise data,enterprise data,data aggregation,data meshing linked enterprise data Poster/Demo Session, Semantic Web Challenge and Reception 2012-11-13T18:30:00+05:00 2012-11-13T21:00:00+05:00 Arne Peters b6bb83df9d222533f57914ce8deb43f53e7d5699 University of Koblenz and Landau Peters Arne Arne Peters Todd Minning e963063b77dfbd04bc7955426944af0a74d8e606 University of Georgia Minning Todd Todd Minning Ivan Cantador b16b236b808b9772d4dba554486b73a41b572ad1 Universidad Autonoma de Madrid Cantador Ivan Ivan Cantador Blazej Bulka 660886530570cd0c43286de5309ef2f90404d7ab Clark & Parsia Bulka Blazej Blazej Bulka UNI Klagenfurt UNI Klagenfurt distant supervision IE information extraction Freebase Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web web scale IE information extraction Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web rule based RE We present a large-scale relation extraction (RE) system which learns grammar-based RE rules from the Web by utilizing large numbers of relation instances as seed. Our goal is to obtain rule sets large enough to cover the actual range of linguistic variation, thus tackling the long-tail problem of real-world applications. A variant of distant supervision learns several relations in parallel, enabling a new method of rule filtering. The system detects both binary and n-ary relations. We target 39 relations from Freebase, for which 3M sentences extracted from 20M web pages serve as the basis for learning an average of 40K distinctive rules per relation. Employing an efficient dependency parser, the average run time for each relation is only 19 hours. We compare these rules with ones learned from local corpora of different sizes and demonstrate that the Web is indeed needed for a good coverage of linguistic variation. Freebase relation extraction information extraction, IE, relation extraction, RE, rule based RE, web scale IE, distant supervision, Freebase web scale IE Sebastian Krause, Hong Li, Hans Uszkoreit and Feiyu Xu relation extraction 76490257 IE RE RE 76490257 rule based RE Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web distant supervision Richard Boyce Richard Boyce University of Pittstburgh 66afecdfcc01d6da44ec673d887456361bc85ef3 Richard Boyce Christian Bizer Bizer Free University of Berlin Christian Christian Bizer 2429456d56d1f93b71e1f87c26dc3e9acc88ea49 Shonali Krishnaswamy f6ef54138de4ab56a6b07816c9377dac695a256a Monash University Krishnaswamy Shonali Shonali Krishnaswamy Tutorial Organizer Leora Morgenstern 8f193fe7daa2375be876241e0ee91eadc2775510 SAIC Morgenstern Leora Leora Morgenstern Ruben Verborgh Ghent University fb22bc1100f1f5b282380024f58bf4e906fd3e69 Verborgh Ruben Verborgh Ruben 4c3085b2c9a9fa05ddd0b0148fba4994b101ad7a Lehigh University Dezhao Song Dezhao Dezhao Song 824fc6ddb4837dfb6a25ed5077be0bddce8aa377 Song 2 RDF Query Processing RDF Query Processing Cloud Systems Cloud Systems Cloud computing platforms such as Amazon’s EC2 and Hadoop have had significant adoption as large scale data processing platforms. Their attraction is the possibility of fault-tolerant execution and elastic scaling up or down of resources based on user requirements with minimal administrative burden to users. The rapid surge in volume of available RDF data has sharpened focus on the issue of large scale processing of RDF, making RDF query processing in the cloud an important topic. RDF data processing in the cloud requires considerations about appropriate storage models and computing environments and the impact of their underlying assumptions on RDF‘s schema-last and join-intensive workloads. Some example considerations are the absence of indexes and statistics usually relied on by traditional query optimization techniques and the heavy materialization in runtime execution environments such as Hadoop vs. pipelined execution plans used in traditional data processing systems. This tutorial will cover RDF query processing in the cloud, particularly MapReduce execution platforms such as Hadoop. It will overview the basics of cloud computing and cloud data storage systems, optimization issues and the state-of-the-art in RDF query optimization for cloud systems. It will also discuss future directions and open issues in query optimization for MapReduce environments. RDF Query Processing RDF Query Processing, Cloud Systems, MapReduce, Query Optimization, RDF Query Processing 2012-11-11T17:30:00+05:00 RDF Query Processing in the Cloud MapReduce Query Optimization CLOUD RDF Query Processing Query Optimization MapReduce 2012-11-11T09:00:00+05:00 linked data entity mining semantic search X-ENS: Semantic Enrichment of Web Search Results at Real-Time X-ENS: Semantic Enrichment of Web Search Results at Real-Time Pavlos Fafalios and Yannis Tzitzikas entity mining web search linked data web search semantic search While more and more semantic data are published on the Web, an important question is how typical web users can access and exploit this body of knowledge. Although, existing interaction paradigms in semantic search hide the complexity behind an easy-to-use interface, they have not managed to cover common search needs. In this paper, we present X-ENS (eXplore ENtities in Search), a web search application that enhances the classical, keyword-based, web searching with semantic information, as a means to combine the pros of both Semantic Web standards and common Web Searching. X-ENS identifies entities of interest in the snippets of the top search results which can be further exploited in a faceted search-like interaction scheme, and thereby can help the user to limit the - often very large - search space to those hits that contain a particular piece of information. Moreover, X-ENS permits the exploration of the identified entities by exploiting semantic repositories. semantic search,web search,entity mining,linked data X-ENS: Semantic Enrichment of Web Search Results at Real-Time Jean Christoph Jung c7b9c84be52c7c9e0b88a07514137e3a3388206c University of Bremen Jung Jean Jean Christoph Jung Maximilian Nickel Nickel Ludwig Maximilians University of Munich Maximilian 17010f98e03b0056dabe8530aa20d93a9f3a04d5 Maximilian Nickel SRI International SRI International Linked Data at The Open University: From Technical Challenges to Organizational Innovation linked data,education,linked universities,applications linked universities Mathieu d'Aquin and Stuart Brown Linked Data at The Open University: From Technical Challenges to Organizational Innovation Linked Data at The Open University: From Technical Challenges to Organizational Innovation linked data applications linked universities linked data applications education education Thomas Malone Malone Thomas Malone MIT Sloan School of Management Thomas Thomas W. Malone from MIT‘s Sloan School of Management has been confirmed as the first keynote speaker for ISWC 2012. Tom heads the Center for Collective Intelligence at MIT, where he investigates how new organizations can be designed to take advantage of the possibilities provided by information technology. Tom‘s more recent research work studies the future or work and, in particular, the transformative capabilities of collective intelligence. 7d4e8f673f0eddcb502e79be07481c507f9bc7f4 Welty IBM Research Chris 0e3d5ea039c6eb1e869a0fa320904d0984829e82 Chris Welty Chris Welty Antoniou FORTH-ICS f44cd7769f416e96864ac43498b082155196829e Grigoris Grigoris Antoniou Grigoris Antoniou University of Aberdeen University of Aberdeen Epimorphics Epimorphics TU Chemnitz TU Chemnitz Thomas Scharrenbach University of Zurich Scharrenbach Thomas Thomas Scharrenbach Humboldt University of Berlin Hartig Olaf 9c09772d208636b590bf7b41d9d1976b80f6b335 Olaf Hartig Olaf Hartig Hamdi Aloulou 9cd3a3a229fd15b337e3b1e63d5a46c5d28f3bb5 Image & Pervasive Access Lab (IPAL), UMI CNRS Aloulou Hamdi Hamdi Aloulou Bibliographic Data Mining Semantic Relations between Research Areas Data Mining Ontology Population Research Data,Ontology Population,Bibliographic Data,Empirical Evaluation,Scholarly Ontologies, Data Mining Bibliographic Data 76490401 Ontology Population Francesco Osborne and Enrico Motta Empirical Evaluation Research Data Empirical Evaluation Mining Semantic Relations between Research Areas For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the finegrained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore they tend not to cover the most recent research trends. In addition, as we move towards a semantic characterization of these relations, there is arguably a need for a more sophisticated characterization than a homogeneous taxonomy, to reflect the different ways in which research areas can be related. In this paper we propose Klink, a new approach to i) automatically generating relations between research areas and ii) populating a bibliographic ontology, which combines both machine learning methods and external knowledge, which is drawn from a number of resources, including Google Scholar and Wikipedia. We have tested a number of alternative algorithms and our evaluation shows that a method relying on both external knowledge and the ability to detect temporal relations between research areas performs best with respect to a manually constructed standard. Mining Semantic Relations between Research Areas Scholarly Ontologies 76490401 Scholarly Ontologies Research Data Data Mining Nigam Shah Stanford University 40f599c3935f0f17928a090c90140795afcac7ec Shah Nigam Nigam Shah Chinese Academy of Sciences Chinese Academy of Sciences Michael Watzke 0598807bb9ce213574fc095dd76119171b30be94 Siemens AG, Corporate Technology Watzke Michael Michael Watzke Vassilis Christophides FORTH-ICS Christophides Vassilis Vassilis Christophides Irene Irene Celino Celino Politecnico di Milano Irene Celino a62cfa4877ae7d8b225f36c1afeeeec08a5316ae Ludwig Maximilians University of Munich Ludwig Maximilians University of Munich Miriam Fernandez Miriam Fernandez Fernandez KMi, The Open University bf9aae3c7b2fc98e26382c4d558508d885a3850f Miriam Information Sciences Institute, University of Southern California Ambite José José Luis Ambite 1e430f2ad8c3dd42da0ac00ed2c6a7c3e6fcaeb5 José Luis Ambite Linked Open Data Graph navigation languages The main goal of current Web navigation languages is to retrieve set of nodes reachable from a given node. No information is provided about the fragments of the Web navigated to reach these nodes. In other words, information about their connections is lost. This paper presents an efficient algorithm to extract relevant parts of these Web fragments and shows the importance of producing subgraphs besides of sets of nodes. We discuss examples with real data using an implementation of the algorithm in the EXpRESs tool. Valeria Fionda, Claudio Gutierrez and Giuseppe Pirró Subgraph extraction Graph navigation languages,Subgraph extraction,Linked Open Data Extracting Relevant Subgraphs from Graph Navigation Graph navigation languages Subgraph extraction Linked Open Data Extracting Relevant Subgraphs from Graph Navigation Extracting Relevant Subgraphs from Graph Navigation Peter Edwards University of Aberdeen a39911538601368843c38e64f416b47e4be0e936 Peter Edwards Edwards Peter Semantic Web Challenge Corridor-Foyer railings Andriana Gkaniatsou 65a16288cf4bf3bae5d06938e6afbb179c70cd01 University of Edinburgh Gkaniatsou Andriana Andriana Gkaniatsou Paolo Castagna 123092f22cf1830beb449f4ac96fa1bff32ba7c8 Talis Castagna Paolo Paolo Castagna Valentina Maccatrozzo 76500382 Burst the Filter Bubble: Using Semantic Web to Enable Serendipity Personalization techniques aim at helping people dealing with the ever growing amount of information by filtering it according to their interests. However, to avoid the information overload, such techniques often create an over-personalization effect, i.e. users are exposed only to the content systems assume they would like. To break this "personalization bubble" we introduce the notion of serendipity as a performance measure for recommendation algorithms. For this, we first identify aspects from the user perspective, which can determine level and type of serendipity desired by users. Then, we propose a user model that can facilitate such user requirements, and enables serendipitous recommendations. The use case for this work focuses on TV recommender systems, however the ultimate goal is to explore the transferability of this method to different domains. This paper covers the work done in the first eight months of research and describes the plan for the entire PhD trajectory. 76500382 Burst the Filter Bubble: Using Semantic Web to Enable Serendipity Burst the Filter Bubble: Using Semantic Web to Enable Serendipity Free University of Berlin Free University of Berlin schema matching Ontology matching is a key interoperability enabler for the Semantic Web, as well as a useful tactic in some classical data integration tasks dealing with the semantic heterogeneity problem. It takes the ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. These correspondences can be used for various tasks, such as ontology merging, data translation, query answering or navigation on the web of data. Thus, matching ontologies enables the knowledge and data expressed in the matched ontologies to interoperate. The Seventh International Workshop on Ontology Matching Ontology Alignment Evaluation Initiative (OAEI) ontology alignment 2012-11-11T17:30:00+05:00 OM-2012 data interlinking ontology alignment ontology matching ontology matching semantic heterogeneity semantic heterogeneity Ontology Alignment Evaluation Initiative (OAEI) schema matching data interlinking 2012-11-11T09:00:00+05:00 ontology matching, semantic heterogeneity, Ontology Alignment Evaluation Initiative (OAEI), ontology alignment, schema matching, data interlinking ISTI-CNR ISTI-CNR Bastien Rance 909b0a734830ebcf7821071a6a9e60d763494408 National Library of Medicine Rance Bastien Bastien Rance Tutorial Organizer Gerd Gröner University of Koblenz and Landau Gröner Gerd Gerd Gröner Amal Amal Zouaq Amal Zouaq c3bf2cac63d1143642d4108acb2fc9abc10c9ba6 Royal Military College of Canada Zouaq 2012-11-13T17:30:00+05:00 2012-11-13T16:00:00+05:00 In this session, authors of a submission to the posters and demonstration track as well as the Semantic Web Challenge provide a 1 minute teaser for their poster or demo. This year, we have an exciting mix of submissions treating questions such as "How can we interact efficiently with Linked Data?", "How can we visualize data in the Semantic Web?" or "How can we query and analyze Semantic Web data? Poster, Demo, and SWC Minute Mandess Universidad Politecnica de Madrid Universidad Politecnica de Madrid Giorgos Stoilos bccc3af684dc47522826944b0646170213c93b7e National Technical University of Athens Stoilos Giorgos Giorgos Stoilos Elizabeth Daly e5dfa2c5450996c2665562bf24926e9793007af1 IBM Research Daly Elizabeth Elizabeth Daly Yuki Yamagata 01410a79943f8abcd9ab91ea078bdc13e222052a Osaka University Yamagata Yuki Yuki Yamagata Matthew Perry Matthew Oracle 9a80a79242529f41c8d7478d2d29dbe243392954 Matthew Perry Perry 81bc18b08295b9b05944ffc7b9d2f2363d714f13 University of Bari University of Bari 1 Université de Montréal Université de Montréal Kalyanpur Aditya Kalyanpur IBM Research Aditya Kalyanpur Aditya b1fe362f3eabc1dac5c9829fb53fd2de0d556f5b Sugar Labs Sugar Labs Halpin c5e75a0dd882184416c8680f5c402a261314bb79 Harry Halpin Harry Halpin Harry University of Edinburgh