morph-kgc 2.10.0
Powerful [R2]RML engine to create RDF knowledge graphs from heterogeneous data sources.
Requires Python: >=3.9
Keywords: Data Integration, Knowledge Graph, Morph-KGC, R2RML, RDF, RML, RML-star

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**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 UDFs**.
- [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/), [Snowflake](https://www.snowflake.com/).
    - **Property graph databases**: [Neo4j](https://neo4j.com/), [Kùzu](https://github.com/kuzudb/kuzu).

## Documentation :bookmark_tabs:

**[Read the documentation](https://morph-kgc.readthedocs.io)**.

## 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/en/stable/documentation/#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/) - [julian.arenas.guerrero@upm.es](mailto:julian.arenas.guerrero@upm.es)**

*[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 **[SWJ paper](https://www.doi.org/10.3233/SW-223135)**:

```bib
@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:

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