Correcting Knowledge Base Assertions
  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.
