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:
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:
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},
year = {2024},
volume = {15},
number = {1},
pages = {1-20},
issn = {2210-4968},
publisher = {IOS Press},
doi = {10.3233/SW-223135}
}
Sponsor :shield:
1 maintainer
Metadata
1 maintainer
Release files for morph-kgc 2.10.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| morph_kgc-2.10.0.tar.gz | 257.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| morph_kgc-2.10.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 329.8 kB
Release files / morph_kgc-2.10.0.tar.gz
| Download URL | morph_kgc-2.10.0.tar.gz |
|---|---|
| Size | 257.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a8d01d4c2118821ed46490bc6445d0d06f0f5dd23e29eeeee1da64ea99e1a124
|
|
BLAKE2b-256 checksum How to use checksums |
09b12306c3bf858186c1cb1b872fbae45ad5b820a2af7d5755766b9ad91b087a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.2
|
Release files / morph_kgc-2.10.0-py3-none-any.whl
| Download URL | morph_kgc-2.10.0-py3-none-any.whl |
|---|---|
| Size | 72.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
8ce01d8db014a3cbe9d2e77f61c543ea8d19a7f9ec7cd6ad4abf203bd9ce9ba7
|
|
BLAKE2b-256 checksum How to use checksums |
c9c48edb7ec3b0602ffcfad2670cfb5eb2af2c40196695b440b636fc5a50b311
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.2
|
