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https://github.com/morph-kgc/morph-kgc
Powerful RDF Knowledge Graph Generation with RML Mappings
https://github.com/morph-kgc/morph-kgc
data-engineering data-integration database etl knowledge-graph python r2rml rdf rdf-star rml
Last synced: about 1 month ago
JSON representation
Powerful RDF Knowledge Graph Generation with RML Mappings
- Host: GitHub
- URL: https://github.com/morph-kgc/morph-kgc
- Owner: morph-kgc
- License: apache-2.0
- Created: 2020-11-11T11:54:10.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-10-21T11:00:01.000Z (2 months ago)
- Last Synced: 2024-11-09T05:05:53.681Z (about 1 month ago)
- Topics: data-engineering, data-integration, database, etl, knowledge-graph, python, r2rml, rdf, rdf-star, rml
- Language: Python
- Homepage: https://morph-kgc.readthedocs.io
- Size: 32.8 MB
- Stars: 189
- Watchers: 13
- Forks: 34
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-kgc-tools - Morph-KGC - R2RML, RML and RML-star processor to generate RDF and RDF-star knowledge graphs from heterogeneous data sources at scale. (KGC Materializers)
README
[](https://github.com/morph-kgc/morph-kgc/blob/main/LICENSE)
[](https://zenodo.org/badge/latestdoi/311956260)
[](https://pypi.python.org/pypi/morph-kgc)
[](https://pypi.python.org/pypi/morph-kgc)
[](https://pypi.python.org/pypi/morph-kgc)
[](https://github.com/morph-kgc/morph-kgc/actions/workflows/ci.yml)
[](https://morph-kgc.readthedocs.io/en/stable/?badge=latest)
[](https://colab.research.google.com/drive/1ByFx_NOEfTZeaJ1Wtw3UwTH3H3-Sye2O?usp=sharing)
**Morph-KGC** is an engine that constructs **[RDF](https://www.w3.org/TR/rdf11-concepts/)** knowledge graphs from heterogeneous data sources with the **[R2RML](https://www.w3.org/TR/r2rml/)** and **[RML](https://w3id.org/rml/core/spec)** mapping languages. Morph-KGC is built on top of [pandas](https://pandas.pydata.org/) and it leverages *mapping partitions* to significantly reduce execution times and memory consumption for large data sources.
## Features :sparkles:
- User-friendly mappings with **[YARRRML](https://rml.io/yarrrml/spec/)**.
- Transformation functions with **[RML-FNML](https://w3id.org/rml/fnml/spec)**, including **Python user-defined functions**.
- [RDF-star](https://w3c.github.io/rdf-star/cg-spec/2021-12-17.html) generation with **[RML-star](https://w3id.org/rml/star/spec)**.
- **[RML views](https://2023.eswc-conferences.org/wp-content/uploads/2023/05/paper_Arenas-Guerrero_2023_Boosting.pdf)** over tabular data sources and [JSON](https://www.json.org) files.
- Integration with **[RDFLib](https://rdflib.readthedocs.io)**, **[Oxigraph](https://pyoxigraph.readthedocs.io/en)** and [Kafka](https://kafka-python.readthedocs.io).
- **Optimized** to materialize large knowledge graphs.
- **Remote** data and mapping files.
- Input data formats:
- **Relational databases**: [MySQL](https://www.mysql.com/), [PostgreSQL](https://www.postgresql.org/), [Oracle](https://www.oracle.com/database/), [Microsoft SQL Server](https://www.microsoft.com/sql-server), [MariaDB](https://mariadb.org/), [SQLite](https://www.sqlite.org).
- **Tabular files**: [CSV](https://en.wikipedia.org/wiki/Comma-separated_values), [TSV](https://en.wikipedia.org/wiki/Tab-separated_values), [Excel](https://www.microsoft.com/en-us/microsoft-365/excel), [Parquet](https://parquet.apache.org/documentation), [Feather](https://arrow.apache.org/docs/python/feather.html), [ORC](https://orc.apache.org/), [Stata](https://www.stata.com/), [SAS](https://www.sas.com), [SPSS](https://www.ibm.com/analytics/spss-statistics-software), [ODS](https://en.wikipedia.org/wiki/OpenDocument).
- **Hierarchical files**: [JSON](https://www.json.org), [XML](https://www.w3.org/TR/xml/).
- **In-memory data structures**: [Python Dictionaries](https://docs.python.org/3/tutorial/datastructures.html#dictionaries), [DataFrames](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html).
- **Cloud data lake solutions**: [Databricks](https://www.databricks.com/).
- **Property graph databases**: [Neo4j](https://neo4j.com/), [Kùzu](https://kuzudb.com).
## Documentation :bookmark_tabs:
**[Read the documentation](https://morph-kgc.readthedocs.io/en/stable/documentation/)**.
## Tutorial :woman_teacher:
Learn quickly with the tutorial in **[Google Colaboratory](https://colab.research.google.com/drive/1ByFx_NOEfTZeaJ1Wtw3UwTH3H3-Sye2O?usp=sharing)**!
## Getting Started :rocket:
**[PyPi](https://pypi.org/project/morph-kgc/)** is the fastest way to install Morph-KGC:
```bash
pip install morph-kgc
```
We recommend to use [virtual environments](https://docs.python.org/3/library/venv.html#) to install Morph-KGC.
To run the engine via **command line** you just need to execute the following:
```bash
python3 -m morph_kgc config.ini
```
Check the **[documentation](https://morph-kgc.readthedocs.io/endocumentation/#configuration)** to see how to generate the configuration **INI file**. **[Here](https://github.com/morph-kgc/morph-kgc/blob/main/examples/configuration-file/default_config.ini)** you can also see an example INI file.
It is also possible to run Morph-KGC as a **library** with **[RDFLib](https://rdflib.readthedocs.io)** and **[Oxigraph](https://pyoxigraph.readthedocs.io/en)**:
```python
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](https://github.com/morph-kgc/morph-kgc/blob/main/LICENSE)**.
## Author & Contact :mailbox_with_mail:
- **[Julián Arenas-Guerrero](https://github.com/arenas-guerrero-julian/) - [[email protected]](mailto:[email protected])**
*[Ontology Engineering Group](https://oeg.fi.upm.es)*, *[Universidad Politécnica de Madrid](https://www.upm.es/internacional)*.
## Citing :speech_balloon:
If you used Morph-KGC in your work, please cite the **[SoftwareX](https://www.sciencedirect.com/science/article/pii/S2352711024000803)** or **[SWJ](https://www.doi.org/10.3233/SW-223135)** papers:
```bib
@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}
}
```
## Sponsor :shield:
