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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 with one set of literal assertions from DBpedia, one set of entity assertions from an enterprise medical KB, and one set of mapping assertions from a music KB constructed by integrating Wikidata, Discogs and MusicBrainz. It has achieved promising results, with a correction rate (i.e., the ratio of the target assertions\/alignments that are corrected with right substitutes) of 70.1 %, 60.9 % and 71.8 %, respectively.<\/jats:p>","DOI":"10.3233\/sw-210448","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T19:07:49Z","timestamp":1634152069000},"page":"29-53","source":"Crossref","is-referenced-by-count":2,"title":["An assertion and alignment correction framework for large scale knowledge bases"],"prefix":"10.1177","volume":"14","author":[{"given":"Jiaoyan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Oxford, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ernesto","family":"Jim\u00e9nez-Ruiz","sequence":"additional","affiliation":[{"name":"City, University of London, London, UK"},{"name":"Centre for Scalable Data Access (SIRIUS), University of Oslo, Oslo, 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