Correcting Knowledge Base Assertions

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Authors Ernesto Jimenez-Ruiz, Erik B. Myklebus, Xi Chen, Ian Horrocks, Jiaoyan Chen
Journal/Conference Name The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
Paper Category
Paper 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.
Date of publication 2020
Code Programming Language Python
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