Journal
SEMANTIC WEB - ISWC 2019, PT I
Volume 11778, Issue -, Pages 110-127Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-30793-6_7
Keywords
Knowledge base correction; Literal canonicalization; Knowledge-based learning; Recurrent Neural Network
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Funding
- AIDA project
- Alan Turing Institute under the EPSRC grant [EP/N510129/1]
- SIRIUS Centre for Scalable Data Access (Research Council of Norway) [237889]
- Royal Society
- EPSRC
- EPSRC [EP/L012138/1] Funding Source: UKRI
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Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability are 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.
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