morph-kgc 1.3.2

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

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:
  • Relational databases: MySQL, PostgreSQL, Oracle, Microsoft SQL Server, MariaDB, SQLite.
  • Tabular files: CSV, TSV, Excel, Parquet, Feather, ORC, Stata, SAS, SPSS.
  • Hierarchical files: JSON, XML.
• 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.

Getting Started

Usage

Configuration

• Engine
• Data Sources
  • Relational Databases
  • Data Files

Tutorial

Features

Academic Publications

License

FAQ

Authors

• Julián Arenas-Guerrero ([e-mail])
• David Chaves-Fraga
• Jhon Toledo
• Oscar Corcho

Ontology Engineering Group, Universidad Politécnica de Madrid | 2020 - Present