Correcting Assertions and Alignments of Large Scale Knowledge Bases

Tracking #: 2723-3937

This paper is currently under review
Authors: 
Jiaoyan Chen
Ernesto Jimenez-Ruiz
Ian Horrocks
Xi Chen
Erik Bryhn Myklebust

Responsible editor: 
Guest Editors KG Validation and Quality

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Full Paper
Abstract: 
Various knowledge bases (KBs) have been constructed via information extraction from encyclopedias, text and tables, as well as alignment of multiple sources. Their usefulness and usability is often limited by quality issues. One common issue is the presence of erroneous assertions and alignments, often caused by lexical or semantic confusion. We study the problem of correcting such assertions and alignments, and present a general correction framework which combines lexical matching, context-aware sub-KB extraction, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using three representative large scale KBs: DBpedia, an enterprise medical KB and a music KB constructed by aligning Wikidata, Discogs and MusicBrainz.
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Under Review