Correcting Knowledge Base Assertions
This addresses quality issues in knowledge bases for users relying on accurate data, but it appears incremental as it builds on existing correction techniques.
The paper tackles the problem of erroneous assertions in knowledge bases, which limit their usefulness, by presenting a general correction framework that combines lexical matching, semantic embedding, soft constraint mining, and semantic consistency checking, and it is evaluated on DBpedia and an enterprise medical KB.
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.