Journal
WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020)
Volume -, Issue -, Pages 1537-1547Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3366423.3380226
Keywords
Knowledge Base Quality; Assertion Correction; Semantic Embedding; Constraint Mining; Consistency Checking
Funding
- AIDA project (Alan Turing Institute)
- Samsung Research UK
- Siemens AG
- EPSRC [EP/P025943/1, EP/S032347/1, EP/S019111/1]
- SIRIUS Centre for Scalable Data Access (Research Council of Norway)
- Jarvis Lab Tencent
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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.
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