论文标题
纠正知识库主张
Correcting Knowledge Base Assertions
论文作者
论文摘要
知识库（KB）的有用性和可用性通常受质量问题的限制。一个普遍的问题是存在错误的断言，通常是由词汇或语义混乱引起的。我们研究了纠正此类断言的问题，并提出了一个一般校正框架，该框架结合了词汇匹配，语义嵌入，软约束挖掘和语义一致性检查。使用DBPEDIA和企业Medical KB评估该框架。
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.
