Graph Convolution Network over Dependency Structure Improve Knowledge Base Question Answering

• Published in: Electronics (Basel) 2023-06, Vol.12 (12), p.2675
• Main Authors: Zhang, Chenggong, Zha, Daren, Wang, Lei, Mu, Nan, Yang, Chengwei, Wang, Bin, Xu, Fuyong
• Format: Article
• Language: English
• Subjects: Chan, Jackie; Convolution; Information technology; Knowledge; Knowledge bases (artificial intelligence); Knowledge-based systems; Methods; Natural language; Neural networks; Question-answering systems; Questions; Semantics
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12122675

Knowledge base question answering (KBQA) can be divided into two types according to the type of complexity: questions with constraints and questions with multiple hops of relationships. Previous work on knowledge base question answering have mostly focused on entities and relations. In a multihop question, it is insufficient to focus solely on topic entities and their relations since the relation between words also contains some important information. In addition, because the question contains constraints or multiple relationships, the information is difficult to capture, or the constraints are missed. In this paper, we applied a dependency structure to questions that capture relation information (e.g., constraint) between the words in question through a graph convolution network. The captured relation information is integrated into the question for re-encoding, and the information is used to generate and rank query graphs. Compared with existing sequence models and query graph generation models, our approach achieves a 0.8–3% improvement on two benchmark datasets.