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:

• User-friendly mappings with YARRRML.
• Transformation functions with RML-FNML, including Python UDFs.
• RDF-star generation with RML-star.
• RML views over tabular data sources and JSON files.
• Integration with RDFLib, Oxigraph and Kafka.
• Optimized to materialize large knowledge graphs.
• Remote data and mapping files.
• Input data formats:
  • Relational databases: MySQL, PostgreSQL, Oracle, Microsoft SQL Server, MariaDB, SQLite.
  • Tabular files: CSV, TSV, Excel, Parquet, Feather, ORC, Stata, SAS, SPSS, ODS.
  • Hierarchical files: JSON, XML.
  • In-memory data structures: Python Dictionaries, DataFrames.
  • Cloud data lake solutions: Databricks, Snowflake.
  • Property graph databases: Neo4j, Kùzu.

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: bash 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: bash 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: ```python import morph_kgc

generate the triples and load them to an RDFLib graph

grdflib = morphkgc.materialize('/path/to/config.ini')

work with the RDFLib graph

qres = grdflib.query('SELECT DISTINCT ?classes WHERE { ?s a ?classes }')

generate the triples and load them to Oxigraph

goxigraph = morphkgc.materialize_oxigraph('/path/to/config.ini')

work with Oxigraph

qres = goxigraph.query('SELECT DISTINCT ?classes WHERE { ?s a ?classes }')

the methods above also accept the config as a string

config = """ [DataSource1] mappings: /path/to/mapping/mappingfile.rml.ttl dburl: mysql+pymysql://user:password@localhost:3306/dbname """ grdflib = morph_kgc.materialize(config) ```

License :unlock:

Morph-KGC is available under the Apache License 2.0.

Author & Contact :mailboxwithmail:

• Julián Arenas-Guerrero - julian.arenas.guerrero@upm.es

Ontology Engineering Group, Universidad Politécnica de Madrid.

Citing :speech_balloon:

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

bib @article{arenas2024morph, 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 = {2024}, volume = {15}, number = {1}, pages = {1-20}, issn = {2210-4968}, publisher = {IOS Press}, doi = {10.3233/SW-223135} }

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