Correcting Knowledge Base Assertions
2020-01-19Code Available1· sign in to hype
Jiaoyan Chen, Xi Chen, Ian Horrocks, Ernesto Jimenez-Ruiz, Erik B. Myklebus
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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.