City Research Online

Abstract

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching.

Publication Type:	Article
Additional Information:	Chen, J., Jimenez-Ruiz, E. and Horrocks, I. (2019). Canonicalizing Knowledge Base Literals. In: The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part II. Lecture Notes in Computer Science (11779). . Cham: Springer. ISBN 9783030307950 © Springer Nature Switzerland AG. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-30796-7.
Publisher Keywords:	Knowledge Base Correction, Literal Canonicalization, Knowledge-based Learning, Recurrent Neural Network
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Departments:	School of Science & Technology > Computer Science

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