A simple method for inducing class taxonomies in knowledge graphs

Tracking #: 2708-3922

This paper is currently under review
Authors: 
Marcin Pietrasik
Marek Reformat

Responsible editor: 
Guest Editors ESWC 2020

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Full Paper
Abstract: 
The rise of knowledge graphs as a medium for storing and organizing large amounts of data has spurred research interest in automated methods for reasoning with and extracting information from this form of data. One area which seems to receive less attention is that of inducing a class taxonomy from such graphs. Ontologies, which provide the axiomatic foundation on which knowledge graphs are built, are often governed by a set of class subsumption axioms. These class subsumptions form a class taxonomy which hierarchically organizes the type classes present in the knowledge graph. Manually creating and curating these class taxonomies oftentimes requires expert knowledge and is time costly, especially in large-scale knowledge graphs. Thus, methods capable of inducing the class taxonomy from the knowledge graph data automatically are an appealing solution to the problem. In this paper, we propose a simple method for inducing class taxonomies from knowledge graphs that is scalable to large datasets. Our method borrows ideas from tag hierarchy induction methods, relying on class frequencies and co-occurrences, such that it requires no information outside the knowledge graph's triple representation. Furthermore, we show that the induced hierarchy may be used as a foundation for hierarchical clustering of knowledge graph subjects. We demonstrate the use of our method on four real-world datasets and compare our results with existing tag hierarchy induction methods. We show that our proposed method outperforms existing tag hierarchy induction methods, although both perform well when applied to knowledge graphs.
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