KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis

• URL: http://arxiv.org/abs/2006.00088v3
• Date: Wed, 26 May 2021 15:22:48 GMT
• Title: KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis
• Authors: Filip Ilievski and Daniel Garijo and Hans Chalupsky and Naren Teja Divvala and Yixiang Yao and Craig Rogers and Rongpeng Li and Jun Liu and Amandeep Singh and Daniel Schwabe and Pedro Szekely
• Abstract summary: KGTK is a data science-centric toolkit designed to represent, create, transform, enhance and analyze KGs. 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.
• Score: 9.141014703209494
• License: http://creativecommons.org/licenses/by/4.0/
• 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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