semvec/pypi-packages huggingface dataset:
kgcreator 0.21
Knowledge Graph Creator: converts text to RDF triples
License: Apache License, Version 2.0
Requires Python: >=3.7

# Knowledge Graph Creator command line tool: kgcreator

[![PyPI](https://img.shields.io/pypi/v/kgcreator.svg)](https://pypi.org/project/kgcreator/)
[![Changelog](https://img.shields.io/github/v/release/mark-watson/kgcreator?include_prereleases&label=changelog)](https://github.com/mark-watson/kgcreator/releases)
[![Tests](https://github.com/mark-watson/kgcreator/workflows/Test/badge.svg)](https://github.com/mark-watson/kgcreator/actions?query=workflow%3ATest)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/mark-watson/kgcreator/blob/master/LICENSE)

### Knowledge Graph Creator: converts text to RDF triples and Neo4J graph and Memgraph database data

The Knowledge Graph Creator (kgcreator) is a tool for automating the generation of RDF data for Knowledge Graphs from raw text data read from an input directory path.

The Knowledge Graph Creator creates an output file containing RDF triples suitable for loading into any linked data/semantic web data store.

This Python command line utility is one of the example programs in my book
[Practical Python Artificial Intelligence Programming](https://leanpub.com/pythonai) that is available on [Leanpub.com](https://leanpub.com/pythonai) or available (in the future, this book is under development) to read for free on my web site [https://markwatson.com](https://markwatson.com).


## Installation

Install this tool using `pip`:

    pip install kgcreator
    pip install spacy
    python -m spacy download en_core_web_sm

## Usage

For help, run:

    kgcreator --help
    kgcreator --inputdir=test_data --outputfile=out.rdf  --outputfileneo4j=out.cypher

You can also use:

    python -m kgcreator --help

## Development

To contribute to this tool, first checkout the code. Then create a new virtual environment:

    cd kgcreator
    python -m venv venv
    source venv/bin/activate

Now install the dependencies and test dependencies:

    pip install -e '.[test]'

To run the tests:

    pytest

## Notes on generating Neo4J graph data

Start by identifying:

- Names of entities (node labels).
- Names of relationships.
- Names of properties for nodes and relationships.
- Constraints to be defined.
- Indexes required.
- The most important queries?