Topic enhanced deep structured semantic models for knowledge base question answering

Abstract

Knowledge Base Question Answering (KBQA) is a hot research topic in natural language processing (NLP). The most challenging problem in KBQA is how to understand the semantic information of natural language questions and how to bridge the semantic gap between the natural language questions and the structured fact triples in knowledge base. This paper focuses on simple questions which can be answered by a single fact triple in knowledge base. We propose a topic enhanced deep structured semantic model for KBQA. The proposed method considers the task of KBQA as a matching problem between questions and the subjects and predicates in knowledge base. And the proposed model consists of two stages to match the subjects and predicates, respectively. In the first stage, we propose a Convolutional based Topic Entity Extraction Model (CTEEM) to extract topic entities mentioned in questions. With the extracted entities, we can retrieve the relevant candidate fact triples from knowledge base and obviously decrease the amount of noising candidates. In the second stage, we employ Deep Structured Semantic Models (DSSMs) to compute the semantic relevant score between questions and predicates in the candidates. And we combine the semantic level and the lexical level scores to rank the candidates. We evaluate the proposed method on KBQA dataset released by NLPCC-ICCPOL 2016. The experimental results show that our proposed method achieves the third place among the 21 submitted systems. Furthermore, we also extend the DSSM by using BiLSTM and integrate a convolutional structure on the top of BiLSTM layers. Our experimental results show that the extension models can further improve the performance.