Improve embedded knowledge graph multi-hop question answering based on relational path

Yuanyuan Niu; Yun Jiang; Qingda Zhang

doi:10.1117/12.2656576

20 October 2022 Improve embedded knowledge graph multi-hop question answering based on relational path

Yuanyuan Niu, Yun Jiang, Qingda Zhang

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 1245122 (2022) https://doi.org/10.1117/12.2656576
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

Multi-hop Knowledge Graph Question Answering (KGQA) requires reasoning about multi-hop inference relations between topic entities and answers on the knowledge graph(KG) and returning correct answers. The difficulty of obtaining the implied inference relations in multi-hop questions described in natural language and the sparse knowledge graph bring challenges to the multi-hop KGQA. In this paper, we propose an embedded knowledge graph multi-hop Q&A model based on relational paths, which exploits the relational chains in the knowledge graph and the semantic similarity of multiple questions to improve the accuracy of multi-hop KGQA task. The comparative experiments prove that our method significantly outperforms the state-of-the-art counterparts. Comprehensive ablation experiments also validate the effectiveness of our approach on multi-hop KGQA tasks.

Citation Download Citation

Yuanyuan Niu, Yun Jiang, and Qingda Zhang "Improve embedded knowledge graph multi-hop question answering based on relational path", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 1245122 (20 October 2022); https://doi.org/10.1117/12.2656576

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