kglab

Welcome to Graph Data Science: https://derwen.ai/docs/kgl/

The kglab library provides a simple abstraction layer in Python 3.11-3.13 for building knowledge graphs, leveraging Pandas, NetworkX, RAPIDS, RDFLib, Morph-KGC, and many more.

Getting Started

See the "Getting Started" section of the online documentation.

pip

python3 -m pip install kglab

poetry

poetry add kglab

conda

conda env create -n kglab
conda activate kglab
pip install kglab

Then to run some simple uses of this library:

import kglab

# create a KnowledgeGraph object
kg = kglab.KnowledgeGraph()

# load RDF from a URL
kg.load_rdf("http://bigasterisk.com/foaf.rdf", format="xml")

# measure the graph
measure = kglab.Measure()
measure.measure_graph(kg)

print("edges: {}\n".format(measure.get_edge_count()))
print("nodes: {}\n".format(measure.get_node_count()))

# serialize as a string in "Turtle" TTL format
ttl = kg.save_rdf_text()
print(ttl)

See the tutorial notebooks in the examples subdirectory for sample code and patterns to use in integrating kglab with other graph libraries in Python: https://derwen.ai/docs/kgl/tutorial/

<details> <summary>Using Docker</summary>

For a simple approach to running the tutorials, see use of docker compose: https://derwen.ai/docs/kgl/tutorial/#use-docker-compose

Also, container images for each release are available on DockerHub: https://hub.docker.com/repository/docker/derwenai/kglab

To build a container image and run it for the tutorials:

docker build --pull --rm -f "docker/Dockerfile" -t kglab:latest .
docker run -p 8888:8888 -it kglab

To build and run a container image for testing:

docker build --pull --rm -f "docker/testsuite.Dockerfile" -t kglabtest:latest .
docker run --rm -it kglabtest

</details> <details> <summary>Build Instructions</summary> <strong> Note: unless you are contributing code and updates, in most use cases won't need to build this package locally. </strong>

Instead, simply install from PyPi or use Conda.

To set up the build environment locally, see the "Build Instructions" section of the online documentation.

</details> <details> <summary>Semantic Versioning</summary>

Before <strong>kglab</strong> reaches release <code>v1.0.0</code> the types and classes may undergo substantial changes and the project is not guaranteed to have a consistent API.

Even so, we'll try to minimize breaking changes. We'll also be sure to provide careful notes.

See: changelog.txt

</details> <details> <summary>Contributing Code</summary>

We welcome people getting involved as contributors to this open source project!

For detailed instructions please see: CONTRIBUTING.md

</details> <details> <summary>License and Copyright</summary>

Source code for kglab plus its logo, documentation, and examples have an MIT license which is succinct and simplifies use in commercial applications.

All materials herein are Copyright © 2020-2025 Derwen, Inc.

</details> <details> <summary>Attribution</summary> Please use the following BibTeX entry for citing **kglab** if you use it in your research or software. Citations are helpful for the continued development and maintenance of this library.

@software{kglab,
  author = {Paco Nathan},
  title = {{kglab: a simple abstraction layer in Python for building knowledge graphs}},
  year = 2020,
  publisher = {Derwen},
  doi = {10.5281/zenodo.4416034},
  url = {https://github.com/DerwenAI/kglab}
}

</details>

<img alt="illustration of a knowledge graph, plus laboratory glassware" src="https://raw.githubusercontent.com/DerwenAI/kglab/main/docs/assets/logo.png" width="231" />

Kudos

Many thanks to our open source sponsors; and to our contributors: @ceteri, @dvsrepo, @Ankush-Chander, @louisguitton, @tomaarsen, @Mec-iS, @jake-aft, @cutterkom, @RishiKumarRay, @Tpt, @ArenasGuerreroJulian, @fils, @lapkinvladimir, @akuckartz, @tommybbq, @gauravjaglan, @pebbie, @CatChenal, @dmoore247, plus general support from Derwen, Inc.; the Knowledge Graph Conference and Connected Data World; plus an even larger scope of use cases represented by their communities; and support from Kubuntu Focus, and the RAPIDS team @ NVIDIA.

<img alt="kglab contributors" src="https://contributors-img.web.app/image?repo=derwenai/kglab" />

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