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Powerful [R2]RML engine to create RDF knowledge graphs from heterogeneous data sources.

Project description

morph

License DOI Latest PyPI version Python Version PyPI status build Documentation Status Open In Colab

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.

Features :sparkles:

Documentation :bookmark_tabs:

Read the documentation.

Tutorial :woman_teacher:

Learn quickly with the tutorial in Google Colaboratory!

Getting Started :rocket:

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)

License :unlock:

Morph-KGC is available under the Apache License 2.0.

Author & Contact :mailbox_with_mail:

Ontology Engineering Group, Universidad Politécnica de Madrid.

Citing :speech_balloon:

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}
}

Contributors :woman_technologist:

See the full list of contributors here.

Sponsor :shield:

BASF

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