kglab 0.1.1

Python wrapper for knowledge graph construction tools

kglab

The kglab library provides a simple to use abstraction layer in Python for building knowledge graphs. For several KG projects, we kept reusing a similar working set of libraries:

• rdflib
• networkx
• pyvis
• pytextrank
• richcontext.scholapi
• skosify

Background

Each of those libraries provides a useful piece of the pizzle when you need to leverage knowledge representation, graph algorithms, entity linking, interactive visualization, metadata queries, axioms, etc. However, some of them are relatively low-level (e.g., rdflib) or perhaps not maintained as much (e.g., skosify) and there are challenges integrating them. Challenges we kept having to reinvent work-arounds to resolve.

There are general operations that one must perform on knowledge graphs:

• building triples
• quality assurance (e.g., axioms)
• managing a mix of namespaces
• serialization to/from multiple formats
• interactive visualization
• queries
• graph algorithms
• inference, transitivity, etc.
• embedding
• other ML integrations

The kglab library provides a reasonably "Pythonic" abstraction layer for these operations on KGs. These class definitions can be subclassed and extended to handle more specific needs. Meanwhile, we're also extending some of the key components with distributed versions, based on ray for better use of horizontal scale-out and parallelization.

NB: this repo is UNDER CONSTRUCTION and will undergo much iteration prior to the "KG 101" tutorial at https://www.knowledgeconnexions.world/talks/kg-101/

See wiki for further details.

Installation

Prerequisites:

• Python 3.5+
• rdflib
• NetworkX
• pyvis

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

Tutorial Outline

1. Building a graph in RDF using rdflib

• ex01_0.ipynb
  • examine the dataset
• ex01_1.ipynb
  • construct a graph from RDF triples
  • using multiple namespaces
  • proper handling of literals
  • seralization to strings and files using Turtle and JSON-LD

2. Leveraging the kglab abstraction layer

• ex01_2.ipynb
  • construct and serialize the same graph using kglab

3. Interactive graph visualization with pyvis

• ex01_3.ipynb
  • render triples as an interactive graph

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Production Use Cases

• Derwen and its client projects