A Survey of Question Answering over Knowledge Base

Question Answering over Knowledge Base (KBQA) is a problem that a natural language question can be answered in knowledge bases accurately and concisely. The core task of KBQA is to understand the real semantics of a natural language question and extract it to match in the whole semantics of a knowledge base. However, it is exactly a big challenge due to variable semantics of natural language questions in a real world. Recently, there are more and more out-of-shelf approaches of KBQA in many applications. It becomes interesting to compare and analyze them so that users could choose well. In this paper, we give a survey of KBQA approaches by classifying them in two categories. Following the two categories, we introduce current mainstream techniques in KBQA, and discuss similarities and differences among them. Finally, based on this discussion, we outlook some interesting open problems.