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.

中文翻译：

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纠正知识库断言

知识库 (KB) 的有用性和可用性通常受到质量问题的限制。一个常见问题是存在错误断言，通常是由词汇或语义混淆引起的。我们研究了纠正此类断言的问题，并提出了一个综合了词汇匹配、语义嵌入、软约束挖掘和语义一致性检查的通用纠正框架。该框架使用 DBpedia 和企业医学知识库进行评估。