SYNTAX-BASED GRAPH MATCHING FOR KNOWLEDGE BASE QUESTION ANSWERING

Semantic parsing is a mainstream method of knowledge base question answering task that first generates a set of logical forms according to question and knowledge base (KB), and then selects the most matching one to get answers. However, existing selection methods are usually based on word-level matching, which cannot capture the structural information or solve the long-term dependency problem of entities. To solve this problem, we propose a syntax-based graph matching method, which explicitly models both question and logical form as graphs, and performs matching at both word-level and structure-level. The multi-level matching strategy not only captures the structural and semantic relations between entities but also explores their intrinsic relations. Such a design can greatly minimize the gap between question and most relevant knowledge from KB, and hence can reason more accurately. We conduct extensive experiments on several benchmarks and demonstrate the effectiveness of our proposed method.