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Description:

morphkgc 2.8.0

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:

Supports the R2RML and RML mapping languages.
User-friendly mappings with YARRRML.
Transformation functions with RML-FNML, including Python user-defined functions.
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.
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:
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:

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

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