KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis

TL;DR

KGTK is a comprehensive, data science-oriented toolkit that simplifies the manipulation, transformation, and analysis of large knowledge graphs like Wikidata and DBpedia by leveraging table-based representations and popular data science libraries.

Contribution

The paper introduces KGTK, a unified toolkit that addresses the heterogeneity and complexity of existing KG tools by providing a table-based, scalable, and user-friendly framework for KG operations.

Findings

Successfully integrated large KGs like Wikidata and DBpedia.

Enabled complex KG transformations using familiar data science libraries.

Demonstrated practical applications in real-world scenarios.

Abstract

Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications. While KGs have become a mainstream technology, the RDF/SPARQL-centric toolset for operating with them at scale is heterogeneous, difficult to integrate and only covers a subset of the operations that are commonly needed in data science applications. In this paper we present KGTK, a data science-centric toolkit designed to represent, create, transform, enhance and analyze KGs. KGTK represents graphs in tables and leverages popular libraries developed for data science applications, enabling a wide audience of developers to easily construct knowledge graph pipelines for their applications. We illustrate the framework with real-world scenarios where we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet.