KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis

Image credit: Ron Li


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 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 KGTK with real-world scenarios in which we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet, in our own work.

In Semantic Web Conference 2020
Rongpeng Li (Ron)
Rongpeng Li (Ron)
Business Intelligence Engineer & Author & Speaker

A business insight seeker, an automation geek, a book author and a conference speaker.