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**Morph-KGC** is an engine that constructs **[RDF](https://www.w3.org/TR/rdf11-concepts/)** and **[RDF-star](https://w3c.github.io/rdf-star/cg-spec/2021-12-17.html)** knowledge graphs from heterogeneous data sources with the **[R2RML](https://www.w3.org/TR/r2rml/)**, **[RML](https://rml.io/specs/rml/)** and **[RML-star](https://kg-construct.github.io/rml-star-spec/)** mapping languages. Morph-KGC is built on top of [pandas](https://pandas.pydata.org/) and it leverages *mapping partitions* to significantly reduce execution times and memory consumption for large data sources.
## Features :sparkles:
- Supports **[R2RML](https://www.w3.org/TR/r2rml/)**, **[RML](https://rml.io/specs/rml/)** and **[RML-star](https://kg-construct.github.io/rml-star-spec/)** mapping languages.
- Input data formats:
- **Relational databases**: **[MySQL](https://www.mysql.com/)**, **[PostgreSQL](https://www.postgresql.org/)**, **[Oracle](https://www.oracle.com/database/)**, **[Microsoft SQL Server](https://www.microsoft.com/sql-server)**, **[MariaDB](https://mariadb.org/)**, **[SQLite](https://www.sqlite.org)**.
- **Tabular files**: **[CSV](https://en.wikipedia.org/wiki/Comma-separated_values)**, **[TSV](https://en.wikipedia.org/wiki/Tab-separated_values)**, **[Excel](https://www.microsoft.com/en-us/microsoft-365/excel)**, **[Parquet](https://parquet.apache.org/documentation/latest/)**, **[Feather](https://arrow.apache.org/docs/python/feather.html)**, **[ORC](https://orc.apache.org/)**, **[Stata](https://www.stata.com/)**, **[SAS](https://www.sas.com)**, **[SPSS](https://www.ibm.com/analytics/spss-statistics-software)**, **[ODS](https://en.wikipedia.org/wiki/OpenDocument)**.
- **Hierarchical files**: **[JSON](https://www.json.org)**, **[XML](https://www.w3.org/TR/xml/)**.
- Output **[RDF](https://www.w3.org/TR/rdf11-concepts/)** and **[RDF-star](https://w3c.github.io/rdf-star/cg-spec/2021-12-17.html)** serializations: **[N-Triples](https://www.w3.org/TR/n-triples/)**, **[N-Triples-star](https://w3c.github.io/rdf-star/cg-spec/2021-12-17.html#n-triples-star)**, **[N-Quads](https://www.w3.org/TR/n-quads/)**, **[N-Quads-star](https://w3c.github.io/rdf-star/cg-spec/2021-12-17.html#n-quads-star)**.
- **RML views** over tabular data sources.
- Integration with **[RDFLib](https://rdflib.readthedocs.io)** and **[Oxigraph](https://pyoxigraph.readthedocs.io/en/latest/)**.
- **Remote** data files and mapping files.
- Runs on **Linux**, **Windows** and **macOS** systems.
- **Optimized** to materialize large knowledge graphs.
## Documentation :bookmark_tabs:
**[Read the documentation](https://morph-kgc.readthedocs.io/en/latest/documentation/)**.
## Tutorial :woman_teacher:
Learn quickly with the tutorial in **[Google Colaboratory](https://colab.research.google.com/drive/1ByFx_NOEfTZeaJ1Wtw3UwTH3H3-Sye2O?usp=sharing)**!
## Getting Started :rocket:
**[PyPi](https://pypi.org/project/morph-kgc/)** is the fastest way to install Morph-KGC:
```bash
pip install morph-kgc
```
We recommend to use **[virtual environments](https://docs.python.org/3/library/venv.html#)** to install Morph-KGC.
To run the engine via **command line** you just need to execute the following:
```bash
python3 -m morph_kgc config.ini
```
Check the **[documentation](https://morph-kgc.readthedocs.io/en/latest/documentation/#configuration)** to see how to generate the configuration **INI file**. **[Here](https://github.com/morph-kgc/morph-kgc/blob/main/examples/configuration-file/default_config.ini)** you can also see an example INI file.
It is also possible to run Morph-KGC as a **library** with **[RDFLib](https://rdflib.readthedocs.io)** and **[Oxigraph](https://pyoxigraph.readthedocs.io/en/latest/)**:
```python
import morph_kgc
# generate the triples and load them to an RDFLib graph
g_rdflib = morph_kgc.materialize('/path/to/config.ini')
# work with the RDFLib graph
q_res = g_rdflib.query('SELECT DISTINCT ?classes WHERE { ?s a ?classes }')
# generate the triples and load them to Oxigraph
g_oxigraph = morph_kgc.materialize_oxigraph('/path/to/config.ini')
# work with Oxigraph
q_res = g_oxigraph.query('SELECT DISTINCT ?classes WHERE { ?s a ?classes }')
# the methods above also accept the config as a string
config = """
[DataSource1]
mappings: /path/to/mapping/mapping_file.rml.ttl
db_url: mysql+pymysql://user:password@localhost:3306/db_name
"""
g_rdflib = morph_kgc.materialize(config)
```
## License :unlock:
Morph-KGC is available under the **[Apache License 2.0](https://github.com/morph-kgc/morph-kgc/blob/main/LICENSE)**.
## Author & Contact :mailbox_with_mail:
- **[Julián Arenas-Guerrero](https://github.com/arenas-guerrero-julian/) - [julian.arenas.guerrero@upm.es](mailto:julian.arenas.guerrero@upm.es)**
*[Ontology Engineering Group](https://oeg.fi.upm.es)*, *[Universidad Politécnica de Madrid](https://www.upm.es/internacional)*.
## Citing :speech_balloon:
If you used Morph-KGC in your work, please cite the **[SWJ paper](https://content.iospress.com/download/semantic-web/sw223135?id=semantic-web%2Fsw223135)**:
```bib
@article{arenas2022morph,
title = {{Morph-KGC: Scalable knowledge graph materialization with mapping partitions}},
author = {Arenas-Guerrero, Julián and Chaves-Fraga, David and Toledo, Jhon and Pérez, María S. and Corcho, Oscar},
journal = {Semantic Web},
year = {2022},
doi = {10.3233/SW-223135}
}
```
## Contributors :woman_technologist:
See the full list of contributors **[here](https://github.com/morph-kgc/morph-kgc/graphs/contributors)**.
## Sponsor :shield:
