Abstract
To clean and correct abnormal information in domain-oriented knowledge bases (KBs) such as DBpedia automatically is one of the focuses of large KB correction. It is of paramount importance to improve the accuracy of different application systems, such as Q&A systems, which are based on these KBs. In this paper, a triples correction assessment (TCA) framework is proposed to repair erroneous triples in original KBs by finding co-occurring similar triples in other target KBs. TCA uses two new strategies to search for negative candidates to clean KBs. One triple matching algorithm in TCA is proposed to correct erroneous information, and similar metrics are applied to validate the revised triples. The experimental results demonstrate the effectiveness of TCA for knowledge correction with DBpedia and Wikidata datasets.
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