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Welcome toGraph Data Science:https://derwen.ai/docs/kgl/

Thekglab library provides a simple abstraction layer in Python 3.7+for building knowledge graphs, leveraging Pandas, NetworkX, RAPIDS, RDFLib,Morph-KGC, pythonPSL, and many more.

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 thekglab roadmap.

@kaaloo:

"Feels like it's a Hugging Face for graphs! 🤯"

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

We recommend installing fromPyPi orconda:

python3 -m pip install kglab

pipenv install kglab

conda env create -n kglabconda activate kglabpip install kglab

If you work directly from this Git repo, be sure to install thedependencies:

python3 -m pip install -U pip wheelpython3 -m pip install -r requirements.txt

pipenv install --dev

Alternatively, to install dependencies usingconda:

conda env create -f environment.yml --forceconda activate kglab

Then to run some simple uses of this library:

importkglab# create a KnowledgeGraph objectkg=kglab.KnowledgeGraph()# load RDF from a URLkg.load_rdf("http://bigasterisk.com/foaf.rdf",format="xml")# measure the graphmeasure=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 formatttl=kg.save_rdf_text()print(ttl)

See thetutorial notebooks in theexamples subdirectory forsample code and patterns to use in integratingkglab with othergraph libraries in Python:https://derwen.ai/docs/kgl/tutorial/

WARNING when installing in an existing environment:
Installing a new package in an existing environment may reveal
or create version conflicts. See thekglab requirements
inrequirements.txt before you do. For example, there are
known version conflicts regarding NumPy (>= 1.19.4) andTensorFlow 2+ (~-1.19.2)

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

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

@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.6360664},url ={https://github.com/DerwenAI/kglab}}

Many thanks to our open sourcesponsors;and to our contributors:@ceteri,@dvsrepo,@Ankush-Chander,@louisguitton,@tomaarsen,@Mec-iS,@jake-aft,@Tpt,@ArenasGuerreroJulian,@fils,@cutterkom,@RishiKumarRay,@gauravjaglan,@pebbie,@CatChenal,@jorisSchaller,@dmoore247,plus general support fromDerwen, Inc.;theKnowledge Graph ConferenceandConnected Data World;plus an even larger scope ofuse casesrepresented by their communities;Kubuntu Focus,theRAPIDS team @ NVIDIA,Gradient Flow,andManning Publications.