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    <abstract>Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning. Previous efforts usually exploit large-scale entity-related text corpus or knowledge graph (KG) embeddings as auxiliary information to facilitate answer selection. However, the rich semantics implied in off-the-shelf relation paths between entities is far from well explored. This paper proposes improving multi-hop KGQA by exploiting relation paths’ hybrid semantics. Specifically, we integrate explicit textual information and implicit KG structural features of relation paths based on a novel rotate-and-scale entity link prediction framework. Extensive experiments on three existing KGQA datasets demonstrate the superiority of our method, especially in multi-hop scenarios. Further investigation confirms our method’s systematical coordination between questions and relation paths to identify answer entities.</abstract>
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%0 Conference Proceedings
%T Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs
%A Qiao, Zile
%A Ye, Wei
%A Zhang, Tong
%A Mo, Tong
%A Li, Weiping
%A Zhang, Shikun
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F qiao-etal-2022-exploiting
%X Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning. Previous efforts usually exploit large-scale entity-related text corpus or knowledge graph (KG) embeddings as auxiliary information to facilitate answer selection. However, the rich semantics implied in off-the-shelf relation paths between entities is far from well explored. This paper proposes improving multi-hop KGQA by exploiting relation paths’ hybrid semantics. Specifically, we integrate explicit textual information and implicit KG structural features of relation paths based on a novel rotate-and-scale entity link prediction framework. Extensive experiments on three existing KGQA datasets demonstrate the superiority of our method, especially in multi-hop scenarios. Further investigation confirms our method’s systematical coordination between questions and relation paths to identify answer entities.
%U https://aclanthology.org/2022.coling-1.156
%P 1813-1822

Markdown (Informal)

[Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs](https://aclanthology.org/2022.coling-1.156) (Qiao et al., COLING 2022)

• Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs (Qiao et al., COLING 2022)

ACL

• Zile Qiao, Wei Ye, Tong Zhang, Tong Mo, Weiping Li, and Shikun Zhang. 2022. Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1813–1822, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.