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

• 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.

Organizations that Use Morph-KGC

Licenses

Morph-KGC is available under the Apache License 2.0.

The documentation is licensed under CC BY-SA 4.0.

Author

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

Universidad Politécnica de Madrid.

Citing

If you used Morph-KGC in your work, please cite the SoftwareX or SWJ papers:

@article{arenas2024rmlfnml,
  title = {{An RML-FNML module for Python user-defined functions in Morph-KGC}},
  author = {Julián Arenas-Guerrero and Paola Espinoza-Arias and José Antonio Bernabé-Diaz and Prashant Deshmukh and José Luis Sánchez-Fernández and Oscar Corcho},
  journal = {SoftwareX},
  year = {2024},
  volume = {26},
  pages = {101709},
  issn = {2352-7110},
  publisher = {Elsevier},
  doi = {10.1016/j.softx.2024.101709}
}
@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}
}