City Research Online

Correcting Knowledge Base Assertions

Chen, J., Chen, X., Horrocks, I., Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599 and Myklebus, E. B. (2020). Correcting Knowledge Base Assertions. In: WWW '20: Proceedings of The Web Conference 2020. (pp. 1537-1547). New York, NY, United States: Association for Computing Machinery. ISBN 978-1-4503-7023-3

Abstract

The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2020 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
Publisher Keywords: Knowledge Base Quality, Assertion Correction, Semantic Embed- ding, Constraint Mining, Consistency Checking
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date Deposited: 12 May 2020 16:22
URI: https://openaccess.city.ac.uk/id/eprint/23585
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