kglab 0.2.0

A simple abstraction layer in Python for building knowledge graphs

kglab

The kglab library provides a simple abstraction layer in Python for building knowledge graphs.

Welcome to graph-based data science: https://derwen.ai/docs/kgl/

SPECIAL REQUEST:
Which features would you like in an open source Python library for building knowledge graphs?
Please add your suggestions through this survey:
https://forms.gle/FMHgtmxHYWocprMn6
This will help us prioritize the kglab roadmap.

Getting Started

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

To install from PyPi:

pip install kglab

If you work directly from this Git repo, be sure to install the dependencies as well:

pip install -r requirements.txt

Then to use the library with some simple use cases:

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("```")
print(ttl[:999])
print("```")

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/

Semantic Versioning

Before kglab reaches release v1.0.0 the types and classes may undergo substantial changes and the project is not guaranteed to have a consistent API. Even so, we will try to minimize breaking changes and make careful notes in the changelog.txt file.

Build Instructions

Note: most use cases won't need to build this package locally. Instead, simply install from PyPi or Conda.

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

License and Copyright

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 © 2020-2021 Derwen, Inc.

Attribution

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},
  url = {https://github.com/DerwenAI/kglab}
}

Kudos

Many thanks to our contributors: @ceteri, @jake-aft, @dmoore247, plus general support from Derwen, Inc. and The Knowledge Graph Conference.