Improving Complex Knowledge Base Question Answering with Relation-Aware Subgraph Retrieval and Reasoning Network

• Published in: 2023 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
• Main Authors: Luo, Dan, Sheng, Jiawei, Xu, Hongbo, Wang, Lihong, Wang, Bin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.06.2023
• Subjects: Cognition; Focusing; graph rea-soning; Information retrieval; knowledge base question answering; Knowledge based systems; Network topology; Semantics; Statistical analysis
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN54540.2023.10191339

Complex Knowledge Base Question Answering aims to answer a complex question over a Knowledge Base. A mainstream solution is based on information retrieval, which usually extracts a pivotal subgraph from entire Knowledge Base to locate candidate answers, and then determines the plausible answers with semantic matching between candidate answers and the question. However, such a paradigm can have two critical problems: 1) Complex Knowledge Base Question Answering can be sensitive to the subgraph, since a small subgraph may exclude the answers, while a large one may introduce a lot of noise; 2) directly deriving answers with semantic matching neglects the global topology in the Knowledge Base, which may limit the capability in answer reasoning. To tackle above challenges, we propose the Relation-Aware Subgraph Retrieval and Reasoning Network, where relations are emphasized to construct subgraphs and answer reasoning. Specifically, we present a Relation-Aware Subgraph Retrieval (RASR) method to initialize and prune subgraphs with the guidance of relation semantics. To compre-hensively understand the complex correlations between the question and candidate answers, we put forward a Relation-Aware Reasoning Network (RARN), which contains a text reasoning module focusing on the semantics understanding of the question and a graph reasoning module focusing on mining the topology between the topic entities and the answers. Experiments on two classical benchmark datasets show that our reasoning model outperforms the state-of-the-art results of Information Retrieval models. What's more, data statistical analysis on the subgraphs demonstrates the effectiveness of our proposed subgraph retrieval method.