Relation-Aware Question Answering for Heterogeneous Knowledge Graphs

• URL: http://arxiv.org/abs/2312.11922v1
• Date: Tue, 19 Dec 2023 08:01:48 GMT
• Title: Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
• Authors: Haowei Du, Quzhe Huang, Chen Li, Chen Zhang, Yang Li, Dongyan Zhao
• Abstract summary: Existing retrieval-based approaches solve this task by concentrating on the specific relation at different hops. We claim they fail to utilize information from head-tail entities and the semantic connection between relations to enhance the current relation representation. Our approach achieves a significant performance gain over the prior state-of-the-art.
• Score: 37.38138785470231
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge graph (KG), which requires multiple steps of reasoning. Existing retrieval-based approaches solve this task by concentrating on the specific relation at different hops and predicting the intermediate entity within the reasoning path. During the reasoning process of these methods, the representation of relations are fixed but the initial relation representation may not be optimal. We claim they fail to utilize information from head-tail entities and the semantic connection between relations to enhance the current relation representation, which undermines the ability to capture information of relations in KGs. To address this issue, we construct a \textbf{dual relation graph} where each node denotes a relation in the original KG (\textbf{primal entity graph}) and edges are constructed between relations sharing same head or tail entities. Then we iteratively do primal entity graph reasoning, dual relation graph information propagation, and interaction between these two graphs. In this way, the interaction between entity and relation is enhanced, and we derive better entity and relation representations. Experiments on two public datasets, WebQSP and CWQ, show that our approach achieves a significant performance gain over the prior state-of-the-art. Our code is available on \url{https://github.com/yanmenxue/RAH-KBQA}.

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