Morph-KGC is an engine that constructs RDF knowledge graphs from heterogeneous data sources with the R2RML and RML mapping languages. Morph-KGC is built on top of pandas and it leverages mapping partitions to significantly reduce execution times and memory consumption for large data sources.

Supports the R2RML and RML mapping languages.

Transformation functions with RML-FNML.

RDF-star generation with RML-star.

Human-friendly mappings with YARRRML.

RML views over tabular data sources and JSON files.

Input data formats:

Integration with RDFLib and Oxigraph.

Remote data and mapping files.

Optimized to materialize large knowledge graphs.

Runs on Linux, Windows and macOS systems.

Read the documentation.

Learn quickly with the tutorial in Google Colaboratory!

PyPi is the fastest way to install Morph-KGC:

pip install morph-kgc

We recommend to use virtual environments to install Morph-KGC.

To run the engine via command line you just need to execute the following:

python3 -m morph_kgc config.ini

Check the documentation to see how to generate the configuration INI file. Here you can also see an example INI file.

It is also possible to run Morph-KGC as a library with RDFLib and Oxigraph:

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)

Morph-KGC is available under the Apache License 2.0.

Ontology Engineering Group, Universidad Politécnica de Madrid.

If you used Morph-KGC in your work, please cite the SWJ paper:

@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} }

See the full list of contributors here.