Image & Pervasive Access Lab (IPAL), UMI CNRS
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
GESIS - Leibniz Institute for the Social Sciences
Norwegian University of Science and Technology
Linked Stream Data Processing Engines: Facts and Figures
Danh Le Phuoc, Minh Dao-Tran, Minh-Duc Pham, Peter Boncz, Thomas Eiter and Michael Fink
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.
Kno.e.sis Center, Wright State University
The 2nd International Workshop on Linked Science 2012Tackling Big Data
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 years 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.
Linked Science, Big Data, Linked Data, Open Science, Scientific Communication, Scalability
Information Sciences Institute, University of Southern California
National and Kapodistrian University of Athens
RDFS inference,Parallel reasoning,Scalable reasoning
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.
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.
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 Netscapes 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.
Senior PC Member at ISWC2012(Research Track)
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.
Scalable semantic processing of huge, distributed real-time streams: Semantics Between Event Processing and Cloud Computing
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.
Towards a theoretical foundation for the harmonization of linked data
Linked Data, Data Harmonization, Planning
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
Discoverability,Open Educational Resources,Linked Data,Recommandation,Named Entity Recognition,Application,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.
Mathieu d'Aquin, Carlo Allocca and Trevor Collins
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.
Extracting Justifications from BioPortal Ontologies
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
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.
8th International Workshop on Uncertainty Reasoning for the Semantic Web
Uncertainty reasoning, Uncertainty representation, Imperfect knowledge, Probabilistic ontologies, Fuzzy ontologies
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
SPARQL Update for Complex Event Processing
Complex event processing, SPARQL, RDF, Rete-algorithm
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.
FZI Research Center for Information Technology
INSTANS: High-Performance Event Processing with Standard RDF and SPARQL
Rete,SPARQL,RDF,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.
Mikko Rinne, Esko Nuutila and Seppo Törmä
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.
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
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.
The Trials and Tribulations of a Semantic Technology Evangelist
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.
Tracking Movements and Attention of Crowds in Real Time Analysing Social Streams The case of London 2012 Open Ceremony
stream reasoning,real time analysis,crowd movements,crowd attention,csparql,streaming linked data,linked data,twitter,social media,social media analysis,london2012 olympic games
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.
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
Elena Cabrio, Julien Cojan, Alessio Palmero Aprosio, Bernardo Magnini, Alberto Lavelli and Fabien Gandon
QAKiS: an Open Domain QA System based on Relational Patterns
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.
Detection, Representation, and Exploitation of Events in the Semantic Web
event detection, event representation, event exploitation, multimedia, social media
Composition of Linked Data-based RESTful Services
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.
PC Member at ISWC2012(Semantic Web In Use Track)
Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing with Dementia
Ambient Assisted Living,Context Awareness,Knowledge Modelling,Semantic Web,Inference Engine
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.
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?
A Comparison of Hard Filters and Soft Evidence for Answer Typing in Watson
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.
Chris Welty, J William Murdock, Aditya Kalyanpur and James Fan
Aristotle University of Thessaloniki;Cardiff University
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 Webs 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.
Korea Institute of Science and Technology Information
University of Applied Sciences and Arts Northwestern Switzerland
Data.gov, U.S. General Services Administration
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 NASAs 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.
University of Applied Sciences Western Switzerland
5th International Workshop on Semantic Sensor Networks
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.
semantic sensor networks, ssn, sensor web, SWE, sensor data
GESIS - Leibniz Institute for the Social Sciences; University of Koblenz and Landau
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.
Ana Armas, Bernardo Cuenca Grau and Ian Horrocks
MORe: Modular Combination of OWL Reasoners for Ontology Classification
Ontologies,Ontology Classification,Automated reasoning,Modularity,Semantic Web
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.
EventMedia Live: Exploring Events Connections in Real-Time to Enhance Content
Houda Khrouf, Vuk Milicic and Raphaël Troncy
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.
events,media,semantic web,reconciliation,social services
Centre for Research and Technology Hellas (CERTH)
Achieving Interoperability through Semantics-based Technologies: The Instant Messaging Case
Interoperability,Composition,Ontology,Verification,Mediation,Universal Instant Messaging
Amel Bennaceur, Valerie Issarny, Romina Spalazzese and Shashank Tyagi
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.
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.
The Not-So-Easy Task of Computing Class Subsumptions in OWL RL
RDF rules,SPARQL rules,rule-based inferencing,complexity
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
Irene Celino, Simone Contessa, Marta Corubolo, Daniele Dell'Aglio, Emanuele Della Valle, Stefano Fumeo and Thorsten Krueger
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.
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
Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions
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 Stream,Applied Description Logics,Semantic cities,Semantic integration
Semantic similarity-driven decision support in the skeletal dysplasia domain
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.
Semantic similarity,Decision support,Ontologies
Razan Paul, Tudor Groza, Andreas Zankl and Jane Hunter
Semantic Enrichment by Non-Experts: Usability of Manual Annotation Tools
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.
Annika Hinze, Ralf Heese, Markus Luczak-Rösch and Adrian Paschke
manual semantic annotation,non-expert users,usability
Swiss Federal Institute for Forest, Snow and Landscape Research
Experiences with modeling composite phenotypes in the SKELETOME project
Tudor Groza, Andreas Zankl and Jane Hunter
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
Rafael S. Gonçalves, Bijan Parsia and Ulrike Sattler
Concept-Based Semantic Difference in Expressive Description Logics
OWL Ontologies,Semantic Diff,NCI Thesaurus,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).
Vanessa Lopez, Spyros Kotoulas, Marco Luca Sbodio, Martin Stephenson, Aris Gkoulalas-Divanis and Pol Mac Aonghusa
QuerioCity: A Linked Data Platform for Urban Information Management
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.
Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy
Makoto Nakatsuji, Yasuhiro Fujiwara, Toshio Uchiyama and Hiroyuki Toda
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.
Collaborative filtering,Taxonomy of items,Recommendation over temporal dynamics,Time weight collaborative filtering
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.
Visualization and Presenation of Evolving Knowledge
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 Integration and Analysis over Time
2nd Joint Workshop on Knowledge Evolution and Ontology Dynamics
Representation of and Reasoning on Evolving Knowledge
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
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.
Manuel Salvadores, Matthew Horridge, Paul Alexander, Ray W. Fergerson, Mark A. Musen and Natasha F. Noy
Ontologies,SPARQL,RDF,Biomedical,Linked Data
Using SPARQL to Query BioPortal Ontologies and Metadata
classificational interpretation of mappings
A Formal Semantics for Weighted Ontology Mappings
weighted mappings,degree of satisfiability of a mapping,classificational interpretation of mappings
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.
Manuel Atencia, Alexander Borgida, Jérôme Euzenat, Chiara Ghidini and Luciano Serafini
Milan Dojchinovski, Jaroslav Kuchar, Tomas Vitvar and Maciej Zaremba
Personalised Graph-based Selection of Web APIs
Web APIs, Web services, personalisation, ranking, service selection, social network
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.
Discovering Concept Coverings in Ontologies of Linked Data Sources
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.
Ontology Matching,Schema Alignment,Linked Data
Kouji Kozaki, Hiroko Kou, Yuki Yamagata, Takeshi Imai, Kazuhiko Ohe and Riichiro Mizoguchi
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.
disease ontology,causal chain,ontology visualization
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
ourSpaces - Design and Deployment of a Semantic Virtual Research Environment
Peter Edwards, Edoardo Pignotti, Alan Eckhardt, Kapila Ponnamperuma, Chris Mellish and Thomas Bouttaz
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 todays 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
First Workshop on Programming the Semantic Web
Programming languages for the Semantic Web
Managing the life-cycle of Linked Data with the LOD2 Stack
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,application integration,Provenance
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
Alternative Knowledge Representation Approaches
11th International Semantic Web Conference
PC Member at ISWC2012(Doctoral Consortium)
Creating Enriched YouTube Media Fragments With NERD Using Timed-Text
Yunjia Li, Giuseppe Rizzo and Raphaël Troncy
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.
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.
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
James Michaelis, Deborah L. McGuinness, Cynthia Chang, Joanne Luciano and Jim Hendler
Kostis Kyzirakos, Manos Karpathiotakis and Manolis Koubarakis
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.
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
Valeria Fionda, Claudio Gutierrez and Giuseppe Pirró
Semantic Navigation on the Web with swget
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,Script Language,Web of Data
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
Gabriela Montoya, Maria-Esther Vidal, Oscar Corcho, Edna Ruckhaus and Carlos Buil-Aranda
Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough?
Benchmarking,Federated Semantic Data Query Processing Strategies,SPARQL Endpoints
comparing RDF/S knowledge bases,mapping blank nodes,versioning and synchronization
Blank Node Matching and RDF/S Comparison Functions
Yannis Tzitzikas, Christina Lantzaki and Dimitris Zeginis
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.
Linked Data,Data Catalog,Data integration
Domain Entity Annotation,Linked Open Data,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
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.
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.
benchmark,streaming RDF/SPARQL,streaming data,sensor data
Ying Zhang, Minh-Duc Pham, Oscar Corcho and Jean Paul Calbimonte
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.
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
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.
Josep Maria Brunetti Fernández, Sören Auer and Roberto Garcia
LIPN - UMR 7030 Universit Paris 13 - CNRS
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.
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 users 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,heterogeneous ontology,query translation
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.
Linked Data, Consuming, Data Management, Query Processing, Search, Reasoning
Third International Workshop on Consuming Linked Data
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.
An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices
Machine Perception, Semantic Sensor Web, Sensor Data, Mobile Device, Resource-Constrained Environments
Cory Henson, Krishnaprasad Thirunarayan and Amit Sheth
Fairtrace - Tracing the textile industry
manufacturing process,tracing,business process
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.
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.
Jens Lehmann, Daniel Gerber, Mohamed Morsey and Axel-Cyrille Ngonga Ngomo
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
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.
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
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
On the Diversity and Availability of Temporal Information in Linked Open Data
Anisa Rula, Palmonari Matteo, Andreas Harth, Steffen Stadtmüller and Andrea Maurino
Thomas Steiner, Ruben Verborgh, Raphaël Troncy, Joaquim Gabarro and Rik Van de Walle
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
Hong Kong University of Science and Technology
Carlos Viegas Damásio, Anastasia Analyti and Grigoris Antoniou
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.
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.
Quest: Efficient SPARQL-to-SQL for RDF and OWL
RDB2RDF,R2RML,Direct Mapping,SPARQL,SQL,DBMS,RDBMS,RDF,RDFS,OWL 2,OWL 2 QL,IMDB,Movie Ontology,D2RQ
Mariano Rodriguez-Muro, Josef Hardi and Diego Calvanese
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.
Ontology alignment,Microtask,Crowdsourcing
LEAPS: A Semantic Web and Linked data framework for the Algal Biomass Domain
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.
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.
Ontology Alignment, Schema Matching, Linked Data, Semantic Web
Songyun Duan, Achille Fokoue, Oktie Hassanzadeh, Anastasios Kementsietsidis, Kavitha Srinivas and Michael Ward
Tackling Climate Change: Unfinished Business from the Last "Winter"
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.
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.
Sentiment analysis, semantic concepts, feature interpolation.
DEQA: Deep Web Extraction for Question Answering
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
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.
Question Answering,Web Extraction,Linking,Conceptual Framework,Natural Language Processing,SPARQL
Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures
Link Discovery,Linked Data,Reduction Ratio
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 tools 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.
Iridescent: a Tool for Rapid Semantic Web Service Descriptions
A Machine Learning Approach for Instance Matching Based on Similarity Metrics
Shu Rong, Xing Niu, Evan Wei Xiang, Haofen Wang, Qiang Yang and Yong Yu
Linking Open Data,Instance matching,Similarity matric,Machine learning,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.
Driving Innovation with Open Data and Interoperability
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.
Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids
Semantic Web,complex event processing,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
iExplore: Interactive Browsing and Exploring Biomedical Knowledge
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.
RDF visualization,UMLS,PubMed,knowledge exploration,semantic predication,semantic similarity,Semantic Web
Vinh Nguyen, Olivier Bodenreider, Jagannathan Srinivasan, Todd Minning, Thomas Rindflesch, Bastien Rance, Ramakanth Kavuluru, Hima Yalamanchili, Krishnaprasad Thirunarayan, Satya Sahoo and Amit Sheth
Rapidly Integrating Services into the Linked Data Cloud
Mohsen Taheriyan, Craig Knoblock, Pedro Szekely and José Luis Ambite
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.
Robust Runtime Optimization and Skew-Resistant Execution of Analytical SPARQL Queries on Pig
Spyros Kotoulas, Jacopo Urbani, Peter Boncz and Peter Mika
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.
50 Park Plaza at Arlington Street, Boston, MA 02116
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.
Financial Information Management Using the Semantic Web
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.
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
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/
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
Linked Data,SPARQL,RDF,Distributed Query Processing,Benchmark
SPLODGE: Systematic Generation of SPARQL Benchmark Queries for Linked Open Data
Building Large Scale Relation KB from Text
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.
Linked Data,Relation Extraction,Expectation Maximization
Mark Musen from Stanfords 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.
Get the Google Feeling: Supporting Users in Finding Relevant Sources of Linked Open Data at Web-Scale
Thomas Gottron, Ansgar Scherp, Bastian Krayer and Arne Peters
retrieval,schema index,schema based LOD discovery,user support,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.
Getting to know PROV - the W3C Provenance Specifications
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.
RDFS Reasoning on Massively Parallel Hardware
computational geometry, graph theory, 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.
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.
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
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.
Hitting the Sweetspot: Economic Rewriting of Knowledge Bases
Description Logics,Non-standard Reasoning,Kowledge Base Rewriting,Module Extraction
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.
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.
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
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.
Aidan Hogan, Emir Muñoz and Jürgen Umbrich
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.
linked data,semantic web,similarity,entity search
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 âhot 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
OWL Ontologies,Reasoner Performance Variability,Approximate Reasoning,NCBO BioPortal,Description Logics
rules, ontologies, RIF, SPARQL, declarative logic programs, OWL
Semantic Web Rules: Fundamentals, Applications, and Standards
The area of semantic rules is perhaps the most important frontier today for the Semantic Webs 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.
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
Search, question answering and entity summarization
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%.
Predicting Reasoning Performance Using Ontology Metrics
Yong-Bin Kang, Yuan-Fang Li and Shonali Krishnaswamy
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
Amar Djalil Mezaour, Julien Law-To, Robert Isele, Thomas Schandl and Gerd Zechmeister
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.
linked data,data fusion,data integration,data quality
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
Stéphane Corlosquet, Sudeshna Das, Emily Merrill, Paolo Ciccarese and Tim Clark
Drupal,biomedical,WebID,JSON-LD,RDFa,real-world deployment
Naimdjon Takhirov, Fabien Duchateau and Trond Aalberg
An Evidence-based Verification Approach to Extract Entities and Relations for Knowledge Base Population
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, Knowledge Extraction, Machine Learning
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.
Semantic Web,User Adaptive,Technology Trends Analysis,User Intention,Technology Prediction
InSciTe Adaptive: Intelligent Technology Analysis Service Considering User Intention
Jinhyung Kim, Myunggwon Hwang, Do-Heon Jeong, Sa-Kwang Song and Hanmin Jung
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
Learning on Linked Data: Tensors and their Applications in Graph-Structured Domains
KMi, The Open University;STLab, ISTC-CNR
Thetida Zetta, Efstratios Kontopoulos and Nick Bassiliades
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.
Semantic Web,RuleML,Rule Bases,Rule Editor,Ontologies
Linked Enterprise Data: leveraging the Semantic Web stack in a corporate IS environment
linked data,semantic web,linked enterprise data,enterprise data,data aggregation,data meshing
Poster/Demo Session, Semantic Web Challenge and Reception
Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web
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.
information extraction, IE, relation extraction, RE, rule based RE, web scale IE, distant supervision, Freebase
Sebastian Krause, Hong Li, Hans Uszkoreit and Feiyu Xu
Cloud computing platforms such as Amazons 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 RDFs 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, Cloud Systems, MapReduce, Query Optimization, RDF Query Processing
X-ENS: Semantic Enrichment of Web Search Results at Real-Time
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
Linked Data at The Open University: From Technical Challenges to Organizational Innovation
linked data,education,linked universities,applications
Thomas W. Malone from MITs 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. Toms more recent research work studies the future or work and, in particular, the transformative capabilities of collective intelligence.
Mining Semantic Relations between Research Areas
Research Data,Ontology Population,Bibliographic Data,Empirical Evaluation,Scholarly Ontologies, Data Mining
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.
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.
Graph navigation languages,Subgraph extraction,Linked Open Data
Extracting Relevant Subgraphs from Graph Navigation
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.
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 matching, semantic heterogeneity, Ontology Alignment Evaluation Initiative (OAEI), ontology alignment, schema matching, data interlinking
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?
The Semantic Web, as a field, is undergoing a major shift. After 10 years of mainly foundations-driven research, we now see strong indicators that Semantic Web methods are entering mainstream technology, in a number of forms. The consequent rise in commercial interest will likely have a fundamental impact on the field. Some established research results will make it into mainstream applications. Others will become obsolete. Radically new ideas will emerge. It is thus the right time for the community to contemplate the way ahead. In this workshop, we will provide an exciting forum for the discussion of the future of the Semantic Web. Researchers and practitioners from all corners of the field are invited to provide their insights and projections. The event will focus on discussions and the exchange of ideas, and will use a mix of different styles of interaction between the participants. It is always good to try to look ahead and anticipate the development of a field. For the Semantic Web, it is now particularly important because recent developments indicate that Semantic Web technologies are entering the industrial mainstream. Schema.org and the Facebook Open Graph Protocol are bringing metadata to bear on the Web large-scale. IBMs Watson and Apples Siri incorporate Semantic Technologies. Google is revamping its search approach and is going more semantic in implementing their knowledge graph. And these are just a few of the prominent examples. The commercial uptake will be a game-changer for the field. It seems that only a fraction of the research results of the past ten years are currently being picked up. It seems that shallow semantics brings added value in many, but not all, application areas. In others it seems that there are roadblocks for which deep semantics is required for added value  but current approaches are still limited. Linked Data and Big Data are popular buzzwords right now, but could they be hitting a peak on the expectation curve? If so, what is going to happen in the subsequent dive? If not, how will those areas affect the fields future? In this workshop, we intend to bring together researchers from all corners of the broader Semantic Web community, to share and discuss projections of the way ahead in Semantic Web technologies and knowledge engineering in general.
Semantic Web, Semantic Technologies, future development
Provenance is an increasingly important aspect of data management that is often underestimated and neglected by practitioners. In our work, we target the problem of reconstructing provenance of files in a shared folder setting, assuming that only standard filesystem metadata are available. We propose a content-based approach that is able to reconstruct provenance automatically, leveraging several similarity measures and edit distance algorithms, adapting and integrating them into a multi-signal pipeline. We discuss our research methodology and show some promising preliminary results.
provenance reconstruction, multimodal similarity
information extraction, information consolidation, social media, data enrichment, text mining, linked data
Knowledge Extraction and Consolidation from Social media
This workshop aims to gather innovative approaches for knowledge extraction and consolidation from unstructured social media, in particular from degraded user-generated content (text, images, video) such as tweets, blog posts, forums and user-generated visual media. KECSM will gather novel works from the fields of (a) data analysis and knowledge extraction, and (b) data enrichment, interlinking and consolidation. It will equally consider the application perspective, such as the innovative use of extracted knowledge to navigate, explore or visualise previously unstructured and disparate Web content. The workshop will provide a highly interactive forum for researchers in the fields of semantic and social Web, text mining and NLP, multimedia data analysis, clustering and integration, and ontology and data mapping.
Cross Lingual Semantic Search by Improving Semantic Similarity and Relatedness Measures
Since 2001, the semantic web community has been working hard towards creating standards which will increase the accessibility of available information on the web. Yahoo research recently reported that 30% of all HTML pages contain structured data such as microdata, RDFa, or microformat. Although multilinguality of the web is a hurdle in information access, the rapid growth of the semantic web enables us to retrieve fine grained information across the language barrier. In this thesis, firstly, we focus on developing a methodology to perform cross-lingual semantic search over structured data (knowledge base), by transforming natural language queries into SPARQL. Secondly, we focus on improving the semantic similarity and relatedness measures, to overcome the semantic gap between the vocabulary in the knowledge base and the terms appearing in the query. The preliminary results are evaluated against the QALD-2 test dataset, which achieved a F1 score of 0.46, an average precision of 0.44, and an average recall of 0.48.
NLP, Linked Data, Semantic Web, Knowledge extraction, NLP evaluation metrics, Knowledge modeling
Most of the knowledge available on the Web is encoded in natural language texts or Web documents aimed primarily at human consumption. A promising approach to have programmatic access to such knowledge uses information extraction techniques that aim to extract machine readable structures from free texts, from which it is possible to retrieve entities and categories. These entities can potentially be linked to each other, creating de facto a global knowledge graph of linked entities. The WoLE2012 workshop envisions the Web as a Web of Linked Entities (WoLE), which transparently connects the World Wide Web (WWW) and the Giant Global Graph (GGG) using methods from Information Retrieval (IR) and Natural Language Processing (NLP). The focus of this workshop is to bring together the Information Retrieval, Semantic Web and NLP communities. The primary goal is to strengthen research techniques that provide access to textual information published on the Web to further improve the adoption of Semantic Web technology.
Segreteria SIAT, Autonomous Province of Trento, Italy
Michael Stonebraker has been a pioneer of data base research and technology for more than a quarter of a century. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T. he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column- oriented DBMS, and the H-Store transaction processing engine. Currently, he is working on science-oriented DBMSs, OLTP DBMSs, and scalable data curation. He is the founder of five venture-capital backed startups, which commercialized his prototypes. Presently he serves as Chief Technology Officer of VoltDB and Paradigm4, Inc. Professor Stonebraker is the author of scores of research papers on data base technology, operating systems and the architecture of system software services. He was awarded the ACM System Software Award in 1992, for his work on INGRES. Additionally, he was awarded the first annual Innovation award by the ACM SIGMOD special interest group in 1994, and was elected to the National Academy of Engineering in 1997. He was awarded the IEEE John Von Neumann award in 2005, and is presently an Adjunct Professor of Computer Science at M.I.T, where he is co-director of the new Intel Science and Technology Center focused on big data.
Assessing the quality of data published on the Web has been identified as an essential step in selecting reliable information for use in tasks such as decision making. This paper discusses a quality assessment framework based on semantic web technologies and outlines a role for provenance in supporting and documenting such assessments.
provenance, linked data, quality assessment
ontology patterns, ontology engineering, linked data patterns, knowledge patterns, semantic web design patterns, design patterns
The Workshop on Ontology Patterns is an arena for sharing ideas, innovations, and novel research results concerning patterns for semantic technologies. Patterns for ontology engineering is a large part, but we also welcome reports on other kinds of knowledge and data patterns, design patterns for Semantic Web applications, as well as best practices in general. Patterns need to be shared by a community in order to provide a common language, and to stimulate pattern usage and development - therefore we hope to see you at WOP to discuss your patterns!
recommender systems, linked data, social web, semantic technologies, personalization, big data
More and more semantic data are published following the Linked Data principles, that enable to set up links between objects in different data sources, by connecting information in a single global data space: the Web of Data. Today, Web of Data includes different types of knowledge represented in a homogeneous form: sedimentary one (encyclopedic, cultural, linguistic, common-sense) and real-time one (news, data streams, ...). This data might be useful to interlink diverse information about users, items, and their relations and implement reasoning mechanisms that can support and improve the recommendation process. The challenge is to investigate whether and how this large amount of wide-coverage and linked semantic knowledge can be automatically introduced into systems that perform tasks requiring human-level intelligence. Examples of such tasks include understanding a health problem in order to make a medical decision, or simply deciding which laptop to buy. Recommender systems support users exactly in those complex tasks. The primary goal of the workshop is to showcase cutting edge research on the intersection of Semantic Technologies and Recommender Systems, by taking the best of the two worlds. This combination may provide the Semantic Web community with important real-world scenarios where its potential can be effectively exploited into systems performing complex tasks.
International Workshop on Semantic Technologies meet Recommender Systems & Big Data
Evaluation of techniques for inconsistency handling in OWL 2 QL ontologies
In this paper we present the Quonto Inconsistent Data handler (QuID). QuID is a reasoner for OWL 2 QL that is based on the system Quonto and is able to deal with inconsistent ontologies. The central aspect of QuID is that it implements two different, orthogonal strategies for dealing with inconsistency: ABox repairing techniques, based on data manipulation, and consistent query answering techniques, based on query rewriting. Moreover, by exploiting the ability of Quonto to delegate the management of the ABox to a relational database system (DBMS), such techniques are potentially able to handle very large inconsistent ABoxes. For the above reasons, QuID allows for experimentally comparing the above two different strategies for inconsistency handling in the context of OWL 2 QL. We thus report on the experimental evaluation that we have conducted using QuID. Our results clearly point out that inconsistency-tolerance in OWL 2 QL ontologies is feasible in practical cases. Moreover, our evaluation singles out the different sources of complexity for the data manipulation technique and the query rewriting technique, and allows for identifying the conditions under which one method is more efficient than the other.
Riccardo Rosati, Marco Ruzzi, Mirko Graziosi and Giulia Masotti
OWL,OWL 2 QL,Description Logics,Inconsistency-tolerance
5th International Terra Cognita Workshop 2012
The wide availability of technologies such as GPS, map services and social networks has resulted in the proliferation of geospatial data on the Web. In addition to material produced by professionals (e.g., maps), the public has also been encouraged to make geospatial content, including their geographical location and a record of their outdoor activities, available online. The volume of such user- generated geospatial content is constantly growing. Similarly, the amount of data extracted from the Web and published as Linked Open Data is increasing. Linked Open Data include many data sets with geospatial properties such as coordinates, feature class or topological relations. The geo-referencing of Web resources and users has given rise to various services and applications that exploit it. With the location of users being made available widely, new issues such as those pertaining to security and privacy arise. Likewise, emergency response, context sensitive user applications, and complex GIS tasks all lend themselves toward solutions that combine both the Geospatial Web and the Semantic Web. The workshop will bring together researchers and practitioners from various disciplines, as well as interested parties from industry and government, to advance the frontiers of this exciting research area.
Ludwig Maximilians University of Munich; Siemens AG
The 2012 Joint Workshop on Scalable and High-Performance Semantic Web Systems (SSWS+HPCSW 2012) focuses on addressing the scalability issue with respect to the development and deployment of systems on the Semantic Web. Typically, such systems deal with information described in Semantic Web languages like OWL and RDF(S), and provide services such as storing, reasoning, querying, analysis, etc. There are two basic requirements for these systems. First, they have to satisfy an applications semantic requirements. Second, they must scale well in order to be of practical use. Given the sheer size and distributed nature of the Semantic Web, these requirements impose new challenges beyond those addressed by previous systems. This joint workshop consists of two tracks regarding scalability: one for knowledge base systems, and another for high-performance computing. We expect that the issue of scalability is going to challenge the Semantic Web for a long period of time and significant effort is needed in order to tackle the problem. This joint workshop seeks to bring together researchers and practitioners to share their recent ideas and progress towards building scalable systems for the Semantic Web; participants from related disciplines such as Supercomputing, and High-Performance Computing, Artificial Intelligence, Databases, Information Integration, are welcome.
OWL, RDF, Supercomputing, SPARQL, Linked Data, High-Performance computing
Joint Workshop on Scalable and High-Performance Semantic Web Systems
Kostyantyn Shchekotykhin, Patrick Rodler, Philipp Fleiss and Gerhard Friedrich
Modern ontology debugging methods allow efficient identification and localization of faulty axioms defined by a user while developing an ontology. However, in many use cases such as ontology alignment the ontologies might include many conflict sets, i.e. sets of axioms preserving the faults, thus making ontology diagnosis infeasible. In this paper we present a debugging approach based on a direct computation of diagnoses that omits calculation of conflict sets. Embedded in an ontology debugger, the proposed algorithm is able to identify diagnoses for an ontology which includes a large number of faults and for which application of standard diagnosis methods fails. The evaluation results show that the approach is practicable and is able to identify a fault in adequate time.
Direct computation of diagnoses for ontology alignment
ontology alignment,model-based diagnosis,diagnosis discrimination
Trentino government linked open geo-data: a case study
Our work is settled in the context of the public administration domain, where data can come from different entities, can be produced, stored and delivered in different formats and can have different levels of quality. Hence, such a heterogeneity has to be addressed, while performing various data integration tasks. We report our experimental work on publishing some government linked open geo-metadata and geo-data of the Italian Trentino region. Specifically, we illustrate how 161 core geographic datasets were released by leveraging on the geo-catalogue application within the existing geo-portal. We discuss the lessons we learned from deploying and using the application as well as from the released datasets.
Pavel Shvaiko, Feroz Farazi, Vincenzo Maltese, Alexander Ivanyukovich, Veronica Rizzi, Daniela Ferrari and Giuliana Ucelli
linked data,open government data,publishing pipeline,geographic datasets,semantic geo-catalogue
Many industrial use cases, such as machine diagnostics, can benefit from embedded reasoning, the task of running knowledge-based reasoning techniques on embedded controllers as widely used in industrial automation. However, due to the memory and CPU restrictions of embedded devices like programmable logic controllers (PLCs), state-of-the-art reasoning tools and methods cannot be easily migrated to industrial automation environments. In this paper, we describe an approach to porting lightweight OWL 2 EL reasoning to a PLC platform to run in an industrial automation environment. We report on initial runtime experiments carried out on a prototypical implementation of a PLC-based EL+ -reasoner in the context of a use case about turbine diagnostics.
industrial diagnostics,description logic reasoning,programmable logic controllers
Embedded EL+ Reasoning on Programmable Logic Controllers
Stephan Grimm, Michael Watzke, Thomas Hubauer and Falco Cescolini
L3S Research Center; University of Hannover
Reusing XML Schemas' Information as a Foundation for Designing Domain Ontologies
Designing domain ontologies from scratch is a time-consuming endeavor requiring a lot of close collaboration with domain experts. However, domain descriptions such as XML Schemas are often available in early stages of the ontology development process. For my dissertation, I propose a method to convert XML Schemas to OWL ontologies in an automatic way. The approach addresses the transformation of any XML Schema documents by using the XML Schema metamodel, which is completely represented by the XML Schema Metamodel Ontology. Automatically, all Schema declarations and definitions are converted to class axioms, which are intended to be enriched with additional domain-specific semantic information in form of domain ontologies.
Semantic Web, Ontology Design, OWL, XML, XSD, XSLT
Joint Workshop on Semantic Technologies Applied to Biomedical Informatics and Individualized Medicine (SATBI+SWIM 2012)
The Joint Workshop on Semantic Technologies Applied to Biomedical Informatics and Individualized Medicine, co-located with the 11th International Semantic Web Conference, will bring together researchers, developers and practitioners actively applying Semantic technologies to Biological and Medical Informatics problems. The workshop aims to attract researchers from Computer Science, Information Science, Biomedicine, Bioinformatics, and related areas to share research challenges, results, methods and advances in the appliance of Semantic Web technologies to two domains within Clinical and Translational research  bioinformatics and individualized medicine.
bioinformatics, semantic, ontology, clinical informatics, linked data, personalized medicine
Data provenance is the history of derivation of a data artifact from its original sources. As the real-life provenance records can likely cover thousands of data items and derivation steps, one of the pressing challenges becomes development of formal frameworks for their automated verification. In this paper, we consider data expressed in standard Semantic Web ontology languages, such as OWL, and define a novel verification formalism called provenance specification logic, building on dynamic logic. We validate our proposal by modeling the test queries presented in The First Provenance Challenge, and conclude that the logic core of such queries can be successfully captured in our formalism.
Formal Verification of Data Provenance Records
Szymon Klarman, Stefan Schlobach and Luciano Serafini
provenance,formal verification,description logics,query answering
Data streams are being continually generated in diverse application domains such as traffic monitoring, smart buildings, and so on. Stream Reasoning is the area that aims to combine reasoning techniques with data streams. In this paper, we present our approach to enable rule-based reasoning on semantic data streams using data flow networks in a distributed manner.
Distributed Reasoning on Semantic Data Streams
It has become common to use RDF to store the results of Natural Language Processing (NLP) as a graph of the entities mentioned in the text with the relationships mentioned in the text as links between them. These NLP graphs can be measured with Precision and Recall against a ground truth graph representing what the documents actually say. When asking conjunctive queries on NLP graphs, the Recall of the query is expected to be roughly the product of the Recall of the relations in each conjunct. Since Recall is typically less than one, conjunctive query Recall on NLP graphs degrades geometrically with the number of conjuncts. We present an approach to address this Recall problem by hypothesizing links in the graph that would improve query Recall, and then attempting to find more evidence to support them. Using this approach, we confirm that in the context of answering queries over NLP graphs, we can use lower confidence results from NLP components if they complete a query result.
Chris Welty, Ken Barker, Lora Aroyo and Shilpa Arora
Query Driven Hypothesis Generation for Answering Queries over NLP Graphs
NLP and RDF graphs,Answering conjunctive queries,Hypothesis generation
ourSpaces - A Semantic Virtual Research Environment
provenance,virtual research environment,eResearch
In this demo we present ourSpaces, a semantic Virtual Research Environment designed to support inter-disciplinary research teams. The system utilizes technologies such as OWL, RDF and a rule-based reasoner to support the management of provenance information, social networks, online communication and policy enforcement within the VRE.
First order logic (FOL) rewritability is a desirable feature for query answering over geo-thematic ontologies because in most geoprocessing scenarios one has to cope with large data volumes. Hence, there is a need for combined geo-thematic logics that provide a sufficiently expressive query language allowing for FOL rewritability. The DL-Lite family of description logics is tailored towards FOL rewritability of query answering for unions of conjunctive queries, hence it is a suitable candidate for the thematic component of a combined geo-thematic logic. We show that a weak coupling of DL-Lite with the expressive region connection calculus RCC8 allows for FOL rewritability under a spatial completeness condition for the ABox. Stronger couplings allowing for FOL rewritability are possible only for spatial calculi as weak as the low-resolution calculus RCC2. Already a strong combination of DL-Lite with the low-resolution calculus RCC3 does not allow for FOL rewritability.
FOL rewritability, description logics, region connection calculus, qualitative spatial reasoning, GIS, combined logic
Data Integration,Ontology Matching,Ontology-based Data Integration,SPARQL
Queries, the Missing Link in Automatic Data Integration
This paper introduces the ontology mapping approach of a system that automatically integrates data sources into an ontology-based data integration system (OBDI). In addition to the domain and source ontologies, the mapping algorithm requires a SPARQL query to determine the ontology mapping. Further, the mapping algorithm is dynamic; running each time a query is processed and producing only a partial mapping sufficient to reformulate the query. This approach enables the mapping algorithm to exploit query semantics to correctly choose among ontology mappings that are indistinguishable when only the ontologies are considered. Also, the mapping associates paths with paths, instead of entities with entities. This approach simplifies query reformulation. The sys- tem achieves favorable results when compared to the algorithms developed for Clio, the best automated relational data integration system.
Aibo Tian, Juan F. Sequeda and Daniel Miranker
Entity Coreference, Linked Data, Domain-Independence, Scalability, Candidate Selection, Pruning
Due to the decentralized nature of the Semantic Web, the same real world entity may be described in various data sources and assigned syntactically distinct identifiers. In order to facilitate data utilization in the Semantic Web, without compromising the freedom of people to publish their data, one critical problem is to appropriately interlink such heterogeneous data. This interlinking process can also be referred to as Entity Coreference, i.e., finding which identifiers refer to the same real world entity. This proposal will investigate algorithms to solve this entity coreference problem in the Semantic Web in several aspects. The essence of entity coreference is to compute the similarity of instance pairs. Given the diversity of domains of existing datasets, it is important that an entity coreference algorithm be able to achieve good precision and recall across domains represented in various ways. Furthermore, in order to scale to large datasets, an algorithm should be able to intelligently select what information to utilize for comparison and determine whether to compare a pair of instances to reduce the overall complexity. Finally, appropriate evaluation strategies need to be chosen to verify the effectiveness of the algorithms.
Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages Across Heterogeneous Data Sources
Ontology Constraints in Incomplete and Complete Data
Peter Patel-Schneider and Enrico Franconi
Ontology and other logical languages are built around the idea that axioms enable the inference of new facts about the available data. In some circumstances, however, the data is meant to be complete in certain ways, and deducing new facts may be undesirable. Previous approaches to this issue have relied on syntactically specifying certain axioms as constraints or adding in new constructs for constraints, and providing a different or extended meaning for constraints that reduces or eliminates their ability to infer new facts without requiring the data to be complete. We propose to instead directly state that the extension of certain concepts and roles are complete by making them DBox predicates, which eliminates the distinction between regular axioms and constraints for these concepts and roles. This proposal eliminates the need for special semantics and avoids problems of previous proposals.
Ontologies,Constraints,DBox,Complete information
With the Semantic Web scaling up, and more triple-stores with update facilities being available, the need for higher levels of simultaneous triple-stores with identical information becomes more and more urgent. However, where such Data Replication approaches are common in the database community, there is no comprehensive approach for data replication for the Semantic Web. In this research proposal, we will discuss the problem space and scenarios of data replication in the Semantic Web, and explain how we plan on dealing with this issue.
synchronization, replication, decentralized triple-stores, read-write Web
Proceedings of the Semantic Web Challenge 2012
Very Large Scale OWL Reasoning through Distributed Computation
Due to recent developments in reasoning algorithms of the various OWL profiles, the classification time for an ontology has come down drastically. For all of the popular reasoners, in order to process an ontology, an implicit assumption is that the ontology should fit in primary memory. The memory requirements for a reasoner are already quite high, and considering the ever increasing size of the data to be processed and the goal of making reasoning Web scale, this assumption becomes overly restrictive. In our work, we study several distributed classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.
Description Logics,DL-Lite,Ontology-Based Data Access,Uncertainty in Data,Probabilistic Databases,Data Complexity,Conjunctive Query Answering
Ontology-Based Access to Probabilistic Data with OWL QL
We propose a framework for querying probabilistic instance data in the presence of an OWL2 QL ontology, arguing that the interplay of probabilities and ontologies is fruitful in many applications such as managing data that was extracted from the web. The prime inference problem is computing answer probabilities, and it can be implemented using standard probabilistic database systems. We establish a PTime vs. #P dichotomy for the data complexity of this problem by lifting a corresponding result from probabilistic databases. We also demonstrate that query rewriting (backwards chaining) is an important tool for our framework, show that non-existence of a rewriting into first-order logic implies #P-hardness, and briefly discuss approximation of answer probabilities.
Semantic Web services,topic models,data mining,Web APIs
Chenghua Lin, Yulan He, Carlos Pedrinaci and John Domingue
Feature LDA: a Supervised Topic Model for Automatic Detection of Web API Documentations from the Web
Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labeled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models.
RailGB: Using Open Accessibility Data to Help People with Disabilities
linked data,accessibility,semantic web,open accessibility data
Yunjia Li, E.A. Draffan, Hugh Glaser, Ian Millard, Russell Newman, Mike Wald, Gary Wills and Magnus White
With the fast growth of Linked Data, many datasets have been made available for public access. However, with an increased number of elderly and people with disabilities becoming technologically knowledgeable, it has been noted that few applications have been developed for this population group using Linked Data. In this paper, we present RailGB, a linked-data driven mobile web application to provide dynamic searches for accessible underground stations in London. RailGB may also prove useful for users such as those with heavy loads or baby buggies. Data in RailGB is collected from different datasets and are mashed-up within a user-friendly mobile web interface. A Query Agent is developed to automatically build SPARQL queries linked to different user requirements. Data around access requirements tends to be detailed on a wide range of static web sites and rarely offers options when visiting particular places. RailGB offers a fundamental shift in approach by reacting to the users' needs whilst travelling with choices of access to a range of stations around the particular area to be visited using Linked Data. Further research is required to provide a wider range of information as more high quality open data becomes available.
The Curious Case for Semantics and Linked Data at the Enterprise
RDFa,Microdata,RDF,HTML,SPARQL,Semantic Web,Linked Data,Enterprise,IT,Industry
Financial Market Sensing via Querying a Time-Dependent RDF Graph of Sentiment Indicators Aggregated from Web-Based News Articles
Montiago Labute, Archana Venbakam, Sean Felten and Venkat Krishnamurthy
Semantic Web Services,SSWAP,iPlant,TreeGenes,DiversiTree,CartograTree,OWL
iPlant Semantic Web Platform uses SSWAP (Simple Semantic Web Architecture and Protocol) to enable Semantic Pipelines across Distributed Web and High Performance Computing Resources
Damian Gessler, Blazej Bulka, Evren Sirin, Yan Kang, Pavel Klinov, Hans Vasquez-Gross, John Yu and Jill Wegrzyn
The iPlant Semantic Web Platform uses transaction-time reasoning and just-in-time ontologies to allow users to drag-n-drop independent, distributed semantic web services into a semantic pipeline. The platform uses first-order, description logic reasoning to examine input data types, output data types, and service descriptions to discover services. At each step the user is presented with only those services which operate on the data at that stage in a pipeline. The platform coordinates third-party service invocations, including transaction-time reasoning at third-party web sites. It includes a Just-In-Time ontology editor to allow anyone to host ontologies on our servers; ontologies may also be hosted anywhere on the web. The platform enables TreeGenes, a large biological information resource, to offer single-click marshalling of data and service discovery to semantic pipelines engaging enterprise-class resources at the University of Arizona and high performance computing (HPC) resources at the Texas Advanced Computing Center. The HPC results are sent to other distributed semantic web services, such as phylogenetic tree visualization and University of California CartograTree geographical mapping resource.
Who will follow whom? Exploiting Semantics for Link Prediction in Attention-Information Networks
Social networks,Microblogging,Concept graphs,Graph traversals,Link Prediction
Existing approaches for link prediction, in the domain of network science, exploit a network's topology to predict future connections by assessing existing edges and connections, and inducing links given the presence of mutual nodes. Despite the rise in popularity of Attention-Information Networks (i.e. microblogging platforms) and the production of content within such platforms, no existing work has attempted to exploit the semantics of published content when predicting network links. In this paper we present an approach that fills this gap by a) predicting follower edges within a directed social network by exploiting concept graphs and thereby significantly outperforming a random baseline and models that rely solely on network topology information, and b) assessing the different behavior that users exhibit when making followee-addition decisions. This latter contribution exposes latent factors within social networks and the existence of a clear need for topical affinity between users for a follow link to be created.
Matthew Rowe, Milan Stankovic and Harith Alani
interactive ontology debugging,model-based diagnosis,ontology matching
RIO: Minimizing User Interaction in Ontology Debugging
Interactive ontology debugging incorporates a user who answers queries about entailments of their intended ontology. In order to minimize the amount of user interaction in a debugging session, a user must choose an appropriate query selection strategy. However, the choice of an unsuitable strategy may result in tremendous overhead in terms of time and cost. We present a learning method for query selection which unites the advantages of existing approaches while overcoming their flaws. Our tests show the utility of our approach when applied to a large set of real-world ontologies, its scalability and adequate reaction time allowing for continuous interactivity.
Patrick Rodler, Kostyantyn Shchekotykhin, Philipp Fleiss and Gerhard Friedrich
Using Linked Data To Improve Investment Performance
linked data,rdf,owl,graph data model,investment,portfolio,performance
Aldo Gangemi, Andrea Giovanni Nuzzolese, Valentina Presutti, Francesco Draicchio, Alberto Musetti and Paolo Ciancarini
Automatic typing,Wikipedia,DBpedia,Natural language processing
We present Tipalo, an algorithm and tool for automatically typing DBpedia entities. Tipalo identifies the most appropriate types for an entity by interpreting its natural language definition, which is extracted from its corresponding Wikipedia page abstract. Types are identified by means of a set of heuristics based on graph patterns, disambiguated to WordNet, and aligned to two top-level ontologies: WordNet supersenses and a subset of DOLCE+DnS Ultra Lite classes. The algorithm has been tuned against a golden standard that has been built online by a group of selected users, and further evaluated in a user study.
Kostis Kyzirakos, Manos Karpathiotakis, George Garbis, Charalampos Nikolaou, Konstantina Bereta, Ioannis Papoutsis, Themistoklis Herekakis, Dimitrios Michail, Manolis Koubarakis and Charalambos Kontoes
Wildfire Monitoring,Ontologies,Linked Geospatial Data,RDF SPARQL,Satellite Images
Wildfire Monitoring Using Satellite Images, Ontologies, and Linked Geospatial Data
Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and application domains. When EO data is coupled with other data sources many pionner applications can be developed. In this submission to the semantic web challenge we show how EO data, ontologies, and linked geospatial data can be combined for the development of a wildfire monitoring service that goes beyond applications currently deployed in various EO data centers. The service has been developed in the context of European project TELEIOS that faces head in the challenges of extracting knowledge from EO data, capturing this knowledge by semantic annotation encoded using EO ontologies, and combining these annotations with linked geospatial data to allow the development of interesting applications.
Frank van Harmelen is a professor in Knowledge Representation & Reasoning in the AI department (Faculty of Science) at the Vrije Universiteit Amsterdam. After studying mathematics and computer science in Amsterdam, he moved to the Department of AI in Edinburgh, where he was awarded a PhD in 1989 for his research on meta- level reasoning. While in Edinburgh, he co-developed a logic-based toolkit for expert systems, and worked with Prof. Alan Bundy on proof planning for inductive theorem proving. After his PhD research, he moved back to Amsterdam where he worked from 1990 to 1995 in the SWI Department under Prof. Wielinga, on the use of reflection in expert systems, on the formal underpinnings of the CommonKADS methodology for Knowledge- Based Systems. In 1995 he joined the AI research group at the Vrije Universiteit Amsterdam, where he co-lead the On-To-Knowledge project, one of the first Semantic Web projects. He was appointed full professor in 2002, and is leading the Knowledge Representation and Reasoning Group. He was one of the co-designers of the OWL Web Ontology Language. He is currently scientific director the LarKC project (http://www.larkc.eu), aiming to develop the Large Knowledge Collider, a platform for very large scale semantic web reasoning.
Semantic Vernacular System: an Observation-based, Community-powered, and Semantics-enabled Naming System for Organisms
Han Wang, Nathan Wilson, Kathryn Dunn and Deborah L. McGuinness
The Semantic Vernacular System is a novel naming system for creating named, machine-interpretable descriptions for groups of organisms. Unlike the traditional scientific naming system, which is based on evolutionary relationships, it emphasizes the observable features of organisms. By independently naming the descriptions composed of sets of observational features, as well as maintaining connections to scientific names, it preserves the observational data used to identify organisms. The system is designed to support a peer-review mechanism for creating new names, and uses a controlled vocabulary encoded in the Web Ontology Language to represent the observational features. A prototype of the system is currently under development in collaboration with the Mushroom Observer website. It allows users to propose new names and descriptions, provide feedback on those proposals, and ultimately have them formally approved. This effort aims at offering the mycology community a knowledge base of fungal observational features and a tool for identifying fungal observations.
observation,scientific name,fungus,ontology
On Demand Access to Big Data through Semantic Technologies
Big Data,Federation,Semantic Technologies,Linked Data,Data Integration,Data Analysis,Open Data,Bio2RDF
Hybrid SPARQL queries: fresh vs. fast results
SPARQL,Linked Data,Hybrid SPARQL,Live querying
Jürgen Umbrich, Marcel Karnstedt, Aidan Hogan and Josiane Xavier Parreira
For Linked Data query engines, there are inherent trade-offs between centralised approaches that can efficiently answer queries over data cached from parts of the Web, and live decentralised approaches that can provide fresher results over the entire Web at the cost of slower response times. Herein, we propose a hybrid query execution approach that returns fresher results from a broader range of sources vs. the centralised scenario, while speeding up results vs. the live scenario. We first compare results from two public SPARQL stores against current versions of the Linked Data sources they cache; results are often missing or out-of-date. We thus propose using coherence estimates to split a query into a sub-query for which the cached data have good fresh coverage, and a sub-query that should instead be run live. Finally, we evaluate different hybrid query plans and split positions in a real-world setup. Our results show that hybrid query execution can improve freshness vs. fully cached results while reducing the time taken vs. fully live execution.
Super Stream Collider--Linked Stream Mashups for Everyone
Linked Stream Data,Semantic Sensor,Semantic Streams,Semantic Data Mashup,Linked Sensor Middleware
This paper describes the Super Stream Collider (SSC) platform and tools, which provide a web-based interface and tools for building sophisticated mashups combining seamnitcally annotated Linked Stream and Linked Data sources into easy to use resources for applications. The system includes drag&drop construction tools along with a visual SPARQL/CQELS editor and visualization tools for novice users while supporting full access and control for expert users at the same time. Tied in with this development platform is a cloud deployment architecture which enables the user to deploy the generated mashups into a cloud, thus supporting both the design and deployment of stream-based web applications in a very simple and intuitive way.
Hoan Nguyen Mau Quoc, Martin Serrano, Danh Le Phuoc and Manfred Hauswirth
The Semantic Web Challenge, now in its 10th year, aims to demonstrate practical progress towards achieving the vision of the Semantic Web. The competition, organised in 3 rounds, enables practitioners and scientists to showcase leading-edge real world applications of Semantic Web technology.
While there are many tools and services which support the exploration of research data, by and large these tend to provide a limited set of functionalities, which cover primarily ranking measures and mechanisms for relating authors, typically on the basis of simple co-authorship relations. To try and improve over the current state of affairs, we are developing a novel tool for exploring research data, which is called Rexplore. Rexplore builds on an intelligent algorithm for automatically identifying hierarchical and equivalence relations between research areas, to provide a variety of functionalities and visualizations to help users to make sense of a research area. These include visualizations to detect trends in research; ways to cluster authors according to several dynamic similarity measures; and fine-grained mechanisms for ranking authors, taking into account parameters such as ranking criterion, career stage, calendar years, publication venues, etc.
Research Data,Bibliographic Data,Data Visualization,Data Exploration,Visual Analytics
Linked Open Data cloud,The NCI Thesaurus,Taxonomic Distance Metric,Link Prediction,Graph Summarization,Dense Subgraphs
Mining Patterns from Clinical Trial Annotated Datasets by Exploiting the NCI Thesaurus
Annotations of clinical trials with controlled vocabularies of drugs and diseases, encode scientific knowledge that can be mined to discover relationships between scientific concepts. We present PAnG (Patterns in Annotation Graphs), a tool that relies on dense subgraphs, graph summarization and taxonomic distance metrics, computed using the NCI Thesaurus, to identify patterns.
Joseph Benik, Guillermo Palma, Louiqa Raschid, Andreas Thor and Maria-Esther Vidal
Zhe Wu, Karl Rieb, Jay Banerjee, Vladimir Mikhailov and Sakthi Sundaram
SPARQL,RDF,Enterprise,Linked data,SDCA,Semantic web
Fortifying a SPARQL Endpoint for Enterprise Usage Scenarios
Simplifying MIREOT; a MIREOT Protege Plugin
The Web Ontology Language (OWL) is a commonly used standard for creating ontology artifacts. However, its capabilities for reusing existing OWL artifacts in the creation of new artifacts is limited to the import of whole ontologies, even when only a small handful of classes, object properties, and so on (which we refer to generically as OWL components) are relevant. This situation can result in extremely large and unwieldy, or even broken, ontologies. To address this problem while still promoting ontology reuse, the OBI Consortium has elucidated the Minimum Information to Reference an External Ontology Term (MIREOT). We provide a suite of plugins to the Protege editor that greatly simplifies the use of MIREOT principles during ontology creation and editing.
Josh Hanna, Chen Cheng, Alex Crow, Roger Hall, Jie Liu, Tejaswini Pendurthi, Trent Schmidt, Steven Jennings, Mathias Brochhausen and William Hogan
Proceedings of the ISWC 2012 Posters & Demonstrations Track
Self-medication,Linked Open Data,Semantic Web,RDF,SPARQL,Ontologies
Open Self Medication is a Web application that aims to ac- company the general public in his initiative to self-medicate, i.e. the act of treating undiagnosed medical ailments with unprescribed drugs. The application achieves this goal by providing a set of functionalities that ensure safety and efficiency of this practice. With safety, we mean that the system guides the end-user from a set of common mild medical signs to adapted molecules and drug products, but also highlights the risks, e.g. drug interactions, adverse events, of self prescribing a drug in a given situation. The efficiency argument corresponds to providing a rating, based on a tolerance/efficiency ratio designed by a team of health care professionals, to some identified self-medication molecules. A main characteristic of this application is that almost all the data processed by the system and presented to the end-user comes from a subset of the LOD datasets, namely DrugBank, DailyMed, Sider and DBPedia. This paper motivates the design of such an application, provides the main de- sign choices, describes some implementation details and presents lessons learned and future work.
Cost based Query Ordering over OWL Ontologies
cost-based query planning,static and dynamic ordering,OWL ontologies,conjunctive instance queries,hypertableau reasoners,individual clustering
The paper presents an approach for cost-based query planning for SPARQL queries issued over an OWL ontology using the OWL Direct Semantics entailment regime of SPARQL 1.1. The costs are based on information about the instances of classes and properties that are extracted from a model abstraction built by an OWL reasoner. A static and a dynamic algorithm are presented which use these costs to find optimal or near optimal execution orders for the atoms of a query. For the dynamic case, we improve the performance by exploiting an individual clustering approach that allows for computing the cost functions based on one individual sample from a cluster. Our experimental study shows that the static ordering usually outperforms the dynamic one when accurate statistics are available. This changes, however, when the statistics are less accurate, e.g., due to non-deterministic reasoning decisions.
Christina Lantzaki, Yannis Tzitzikas and Dimitris Zeginis
comparing RDF/S knowledge bases,mapping blank nodes,versioning and synchronization,visualization of delta
Demonstrating Blank Node Matching and RDF/S Comparison Functions
(This demo paper accompanies a research paper accepted for ISWC'2012) The ability to compute the differences that exist between two RDF/S Knowledge Bases (KBs) is important for aiding humans to understand the evolution of knowledge, and for reducing the amount of data that need to be exchanged and managed over the network in order to build synchronization, versioning and replication services. We will show how we can exploit blank node anonymity in order to reduce the delta size when comparing RDF/S KBs. We will show experimental results over real and synthetic data sets that demonstrate significant reductions of the sizes of the computed deltas, and how the reduced deltas can be visualized.
Proceedings of the ISWC 2012 Industry Track
Evaluating Semantic Search Query Approaches with Expert and Casual Users
semantic search,query approaches,evaluation,usability
Khadija Elbedweihy, Stuart Wrigley and Fabio Ciravegna
Usability and user satisfaction are of paramount importance when designing interactive software solutions. Furthermore, the optimal design can be dependent not only on the task but also on the type of user. Evaluations can shed light on these issues; however, very few studies have focused on assessing the usability of semantic search systems. As semantic search becomes mainstream, there is growing need for standardised, comprehensive evaluation frameworks. In this study, we assess the usability and user satisfaction of different semantic search query input approaches (natural language and view-based) from the perspective of different user types (experts and casuals). Contrary to previous studies, we found that casual users preferred the form-based query approach whereas expert users found the graph-based to be the most intuitive. Additionally, the controlled-language model offered the most support for casual users but was perceived as restrictive by experts, thus limiting their ability to express their information needs.
cross-lingual haromonization and reconciliation
cross-lingual and multilingual NLP for the Semantic Web
The vision of the Multilingual Semantic Web workshop series is the creation of a Semantic Web where semantically structured information can be aligned, integrated and used across languages. The workshops are concerned with research questions on how current Semantic Web infrastructure can and should be extended to support this vision. Ontologies and linked data vocabularies are defined often in one language only (English), with a biased semantics and a corresponding world view. An infrastructure should be in place for defining ontologies and vocabularies in multiple languages with a transparent semantics across them. Current Semantic Web representation languages (RDF, OWL, SKOS) are limited in regard of the representation of natural language semantics, leaving much of the semantics hidden in textual web content out of scope for the developing Web of Data. NLP and machine learning for Linked Data can benefit from exploiting linguistic resources such as annotated corpora, Wordnets etc. if they are themselves formally represented and linked by use of Linked Data principles. In addressing such research questions, the workshop aims at providing a forum for researchers at the intersection of NLP, multilingual information access, Linked Data and the Semantic Web to exchange ideas on realizing the Multilingual Semantic Web.
3rd Workshop on the Multilingual Semantic Web
multilinguality, Semantic Web, Multilingual Linked Data, cross-lingual haromonization and reconciliation, cross-lingual and multilingual NLP for the Semantic Web, localization in the Semantic Web
Although the Semantic Web was originally designed as a "web for machines", the growing wealth of information in Linked Open Data has become interesting for human users as well. Consequently, quite a few browsers for Linked Open Data have recently been developed. However, despite being developed for the semantic web, those browsers often present alphabetically ordered lists of facts, without respecting the semantics of the data. In our submission to the Semantic Web Challenge, we present a semantic browser for the semantic web, which aims at presenting facts from Linked Open Data in semantically coherent groups. This paper introduces the main algorithms as well as an evaluation of the browser with end users.
Linked Open Data,User Interface,Browser,Semantic Interface
Workshop participants will discuss sketches of their papers with editors and eidtorial board members of the top Semantic Web jorunals as well as other workshop participants. The goal is to help attendees to turn the sketches into successful submissions and avoid common stumbling blocks. People who wish to participate will be asked to submit a paper sketch (see cfp). The accepted sketeches will be read by the editors and the other chosen participants prior the workshop. Prior to the workshop, participants whose sketches have been accepted to will receive one to three other sketches to read and review. During the workshop, participants will meet in small groups which includes an editorial board memeber. During this discussion, participants will receive specific feedback on on the possible issues to address in order to turn the sketch into a possibly successful submission. Selection for the Journal Workshop Session is competitive. Papers currently under review or revision are excluded.
In this paper we present a demo for efficient detecting of visitors attention in museum environment based on the application of intelligent complex event processing and semantic technologies. The detection takes advantage of semantics: (i) in design time for the correlation of sensors data via modeling of the interesting situations and annotation of artworks and their parts and (ii) in real-time for the more accurate and precise detection of the interesting situation. The results of the proposed approach have been applied in the EU project ARtSENSE.
Demo: Efficient Human Attention Detection in Museums based on Semantics and Complex Event Processing
Sensor,Human attention,Complex Event Processing,Ontologies
Yongchun Xu, Nenad Stojanovic, Ljiljana Stojanovic and Tobias Schuchert
top-k queries,SPARQL query optimization,rank-join
Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solutions (e.g. thousands) even if only a limited number k (e.g. ten) are requested. The SPARQL-RANK algebra is an extended SPARQL algebra that treats order as a first class citizen, enabling efficient split-and-interleave processing schemes that can be adopted to improve the performance of top-k SPARQL queries. In this paper we propose an incremental execution model for SPARQL-RANK queries, we compare the performance of alternative physical operators, and we propose a rank-aware join algorithm optimized for native RDF stores. Experiments conducted with an open source implementation of a SPARQL-RANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude.
Efficient Execution of Top-K SPARQL Queries
Sara Magliacane, Alessandro Bozzon and Emanuele Della Valle
We tackle the problem of improving the relevance of automatically selected tags in large-scale ontology-based information systems. Contrary to traditional settings where tags can be chosen arbitrarily, we focus on the problem of recommending tags (e.g., concepts) directly from a collaborative, user-driven ontology. We compare the effectiveness of a series of approaches to select the best tags ranging from traditional IR techniques such as TF/IDF weighting to novel techniques based on ontological distances and latent Dirichlet allocation. All our experiments are run against a real corpus of tags and documents extracted from the ScienceWise portal, which is connected to ArXiv.org and is currently used by growing number of researchers. The datasets for the experiments are made available online for reproducibility purposes.
Tag Recommendation for Large-Scale Ontology-Based Information Systems
Tag Recommendation,Ontology-Based Information Systems,Experiments
Roman Prokofyev, Alexey Boyarsky, Oleg Ruchayskiy, Karl Aberer, Gianluca Demartini and Philippe Cudré-Mauroux
Irene Celino, Dario Cerizza, Simone Contessa, Marta Corubolo, Daniele Dell'Aglio, Emanuele Della Valle, Stefano Fumeo and Federico Piccinini
The Urbanopoly app is a social, mobile and location-based Game with a Purpose designed around the idea of the "monopoly" board game. Urbanopoly consumes and generates geo-spatial linked and open data; through a Human Computation mechanisms, those data are validated and enriched; new information is published as linked data. Players are thus involved in the geo-spatial data collection according to the "citizens as sensors" approach and the Urbanopoly game is the entertaining expedient to assess and improve geo-spatial linked data quality.
Urbanopoly: Collection and Quality Assessment of Geo-spatial Linked Data via a Human Computation Game
Human Computation,Games with a Purpose,Linked Data,Data Quality,Geo-spatial Data,Volunteered Geographic Information,Provenance
Jena-HBase: A Distributed, Scalable and Efficient RDF Triple Store
Vaibhav Khadilkar, Murat Kantarcioglu, Bhavani Thuraisingham and Paolo Castagna
Lack of scalability is one of the most significant problems faced by single machine RDF data stores. The advent of Cloud Computing and related tools and technologies has paved a way for a distributed ecosystem of RDF triple stores that can potentially allow up to a planet scale storage along with distributed query processing capabilities. Towards this end, we present Jena-HBase, a HBase backed triple store that can be used with the Jena framework. Jena-HBase provides end-users with a scalable storage and querying solution that supports all features from the RDF specification.
Semantic Web,RDF,Jena,Cloud Computing,Hadoop,HBase
Joint Workshop on Large and Heterogeneous Data and Quantitative Formalization in the Semantic Web
Large data, Matching, Meaning, Ontology, Dynamic matching, Semantic Web, quantitative, metrics, analytics, Semantic Information Theory, similarity
One part of this workshop (LHD) is designed to bring together people from different fields working in the area of dynamic matching, interpretation, and integration of heterogeneous data, so that ideas, techniques and problems can be shared and discussed in a broad context. A key part of this workshop is bringing together those from industry and government as well as those from academia. In order to interact successfully in an open and heterogeneous environment, being able to dynamically and adaptively integrate data from other systems on the go is necessary. This may not be a precise process but a matter of finding a good enough understanding to allow interaction to proceed successfully. With the advent of the Web, there are massive amounts of information available online that can assist in this task, but this information is often chaotically organised, stored in a wide variety of data-formats, and difficult to interpret. The other part of this workshop (SemQuant) aims to unlock a new paradigm in Semantic Web research, and even in KR research in general, by adding a quantitative research paradigm to the traditionally predominant qualitative logic-based paradigm. This is motivated in part by the significant growth in Semantic Web data, including ontologies and Linked Data, over recent years. To efficiently manage the vast quantities of knowledge and data on the Semantic Web, we need theories and tools to address questions like: How can we measure knowledge? How are these measurements different from measurements of information? How can we efficiently store knowledge? How can we efficiently and accurately transform knowledge on noisy channels like the Web? How can we measure the quality of ontologies and other forms of knowledge? How can we determine the quality of approximate methods for inference, similarity, soundness, completeness, etc.? How can such quantitative formalization help the engineering and realization of the Semantic Web?
Toward an ecosystem of LOD in the field: LOD content generation and its consuming service
This paper proposes to apply semantic technologies in a new domain, Field research. It is said that if "raw data" is openly available on the Web, it will be used by other people to do wonderful things. But, it would be better to show a use case together with that data, especially in the dawn of LOD. Therefore, we are proceeding with both of LOD content generation and its application for a specific domain. The application addresses an issue of information retrieval in the field, and the mechanism of LOD generation from the Web might be applied to the other domain. Firstly, we demonstrate the use of our mobile application, which searches a plant fitting the environmental conditions obtained by the smartphone's sensors. Then, we introduce our approach of the LOD generation, and present an evaluation showing its practical effectiveness.
Ontology matching,Information retrieval,Domain detection
Kristian Slabbekoorn, Laura Hollink and Geert-Jan Houben
The inherent heterogeneity of datasets on the Semantic Web has created a need to interlink them, and several tools have emerged that automate this task. In this paper we are interested in what happens if we enrich these matching tools with knowledge of the domain of the ontologies. We explore how to express the notion of a domain in terms usable for an ontology matching tool, and we examine various methods to decide what constitutes the domain of a given dataset. We show how we can use this in a matching tool, and study the effect of domain knowledge on the quality of the alignment. We perform evaluations for two scenarios: Last.fm artists and UMLS medical terms. To quantify the added value of domain knowledge, we compare our domain-aware matching approach to a standard approach based on a manually created reference alignment. The results indicate that the proposed domain-aware approach indeed outperforms the standard approach, with a large effect on ambiguous concepts but a much smaller effect on unambiguous concepts.
A Prototype for Semantic based Diagnosis of Road Traffic Congestions
Retrieving the causes of road traffic congestions in quasi real-time is an important task that will enable city managers to get better insight into traffic issues and thus take appropriate corrective actions in a timely way. Our work, accepted at ISWC 2012 In-Use track, tackles this problem by integrating and reasoning over a variety of heterogeneous data sources including data streams. In this paper we present an initial prototype of our work for the city of Dublin, Ireland.
Semantic Data Streams,Semantic Cities,Semantic Data Itegration,Automated Diagnosis,Real World Application of Semantic Web
Marco Luca Sbodio, Freddy Lecue and Anika Schumann
Browsing Linked Open Data with Auto Complete
Linked Open Data,Automatic Completion,Association Rule Mining,Ontology Learning,Ontology Alignment,Lazy Learning
Information in Linked Open Data is incomplete by nature and design. Nevertheless, the more complete information delivered on a subject is, the higher is its value to an end user. With our submission to the Semantic Web Challenge, we show how information, in particular types, can be completed automatically, based on heuristic rule learning. We introduce an approach which employs lazy learning instead of learning a global model, making the approach well scalable to large data, while at the same time providing results at an accuracy of 85.6%. The auto complete function is included in a modular semantic web browser.
Evaluation of a layered approach to question answering over linked data
question answering,linked data,layered approach,experimental evaluation
Sebastian Walter, Christina Unger, Philipp Cimiano and Daniel Bär
We present a question answering system architecture which processes natural language questions in a pipeline consisting of five steps: i) question parsing and query template generation, ii) lookup in an inverted index, iii) string similarity computation, iv) lookup in a lexical database in order to find synonyms, and v) semantic similarity computation. These steps are ordered with respect to their computational effort, following the idea of layered processing: questions are passed on along the pipeline only if they cannot be answered on the basis of earlier processing steps, thereby invoking computationally expensive operations only for complex queries that require them. In this paper we present an evaluation of the system on the dataset provided by the 2nd Open Challenge on Question Answering over Linked Data (QALD-2). The main, novel contribution is a systematic empirical investigation of the impact of the single processing components on the overall performance of question answering over linked data.