Scalable [R2]RML engine to create RDF knowledge graphs from heterogeneous data sources.
Project description
Morph-KGC is an engine that constructs RDF knowledge graphs from heterogeneous data sources with 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.
Main Features
- Supports R2RML and RML mapping languages.
- Input data formats:
- Output RDF serializations: N-Triples, N-Quads.
- Runs on Linux, Windows and macOS systems.
- Compatible with Python 3.7 or higher.
- Optimized to materialize large knowledge graphs.
- Multiple configuration options.
- Available under the Apache License 2.0.
Installation and Usage
PyPi is the fastest way to install Morph-KGC:
pip install morph-kgc
To run the engine you just need to execute the following:
python3 -m morph_kgc configuration.ini
Here you can see how to generate the configuration file. It is also possible to run Morph-KGC as a library with RDFlib:
import morph_kgc
# generate the triples and load them to an RDFlib graph
graph = morph_kgc.materialize('/path/to/configuration.ini')
# work with the graph
graph.query(' SELECT DISTINCT ?classes WHERE { ?s a ?classes } ')
Wiki
Check the wiki with all the information.
Authors
- Julián Arenas-Guerrero (julian.arenas.guerrero@upm.es)
- David Chaves-Fraga
- Jhon Toledo
- Oscar Corcho
Ontology Engineering Group, Universidad Politécnica de Madrid | 2020 - Present
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