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    <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. However, these models fail to utilize information from head-tail entities and the semantic connection between relations to enhance the current relation representation, which undermines the information capturing of relations in KGs. To address this issue, we construct a dual relation graph where each node denotes a relation in the original KG (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.</abstract>
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%0 Conference Proceedings
%T Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
%A Du, Haowei
%A Huang, Quzhe
%A Li, Chen
%A Zhang, Chen
%A Li, Yang
%A Zhao, Dongyan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F du-etal-2023-relation
%X 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. However, these models fail to utilize information from head-tail entities and the semantic connection between relations to enhance the current relation representation, which undermines the information capturing of relations in KGs. To address this issue, we construct a dual relation graph where each node denotes a relation in the original KG (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.
%R 10.18653/v1/2023.findings-emnlp.906
%U https://aclanthology.org/2023.findings-emnlp.906
%U https://doi.org/10.18653/v1/2023.findings-emnlp.906
%P 13582-13592

Markdown (Informal)

[Relation-Aware Question Answering for Heterogeneous Knowledge Graphs](https://aclanthology.org/2023.findings-emnlp.906) (Du et al., Findings 2023)

• Relation-Aware Question Answering for Heterogeneous Knowledge Graphs (Du et al., Findings 2023)

ACL

• Haowei Du, Quzhe Huang, Chen Li, Chen Zhang, Yang Li, and Dongyan Zhao. 2023. Relation-Aware Question Answering for Heterogeneous Knowledge Graphs. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13582–13592, Singapore. Association for Computational Linguistics.