Correcting Knowledge Base Assertions

Chen, Jiaoyan; Chen, Xi; Horrocks, Ian; Jimenez-Ruiz, Ernesto; Myklebust, Erik Bryhn
Chapter
Accepted version
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URI
https://hdl.handle.net/11250/2756012
Date
2020
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Original version
WWW '20: Proceedings of The Web Conference 2020  
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.
Publisher
Association for Computing Machinery (ACM), 2020.
Copyright
© 2020 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal