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arXiv · 2006.00088

KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis

Filip Ilievski·Daniel Garijo·Hans Chalupsky·Naren Teja Divvala·Yixiang Yao·Craig Rogers·Rongpeng Li·Jun Liu·Amandeep Singh·Daniel Schwabe·Pedro Szekely
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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.

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Filip Ilievski, Daniel Garijo, Hans Chalupsky, Naren Teja Divvala, Yixiang Yao, Craig Rogers, Rongpeng Li, Jun Liu, Amandeep Singh, Daniel Schwabe, Pedro Szekely. 2020-05-29. KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis. https://arxiv.org/abs/2006.00088

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