Correcting Assertions and Alignments of Large Scale Knowledge Bases

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.