{"type":"ScholarlyArticle","title":"Two-Phase Hypergraph Based Reasoning with Dynamic Relations for Multi-Hop KBQA","authors":[{"name":"Jiale Han","slug":"jiale-han","given":"Jiale","family":"Han","affiliation":"State Key Laboratory of networking and switching technology , Beijing University of Posts and Telecommunications"},{"name":"Bo Cheng","slug":"bo-cheng","given":"Bo","family":"Cheng","affiliation":"State Key Laboratory of networking and switching technology , Beijing University of Posts and Telecommunications"},{"name":"Xu Wang","slug":"xu-wang","given":"Xu","family":"Wang","affiliation":"State Key Laboratory of networking and switching technology , Beijing University of Posts and Telecommunications"}],"abstract":"Multi-hop knowledge base question answering (KBQA) aims at finding the answers to a factoid question by reasoning across multiple triples. Note that when human performs multi-hop reasoning, one tends to concentrate on specific relation at different hops and pinpoint a group of entities connected by the relation. Hypergraph convolutional networks (HGCN) can simulate this behavior by leveraging hyperedges to connect more than two nodes more than pairwise connection. However, HGCN is for undirected graphs and does not consider the direction of information transmission. We introduce the directed-HGCN (DHGCN) to adapt to the knowledge graph with directionality.  Inspired by human's hop-by-hop reasoning, we propose an interpretable KBQA model based on DHGCN, namely two-phase hypergraph based reasoning with dynamic relations, which explicitly updates relation information and dynamically pays attention to different relations at different hops. Moreover, the model predicts relations hop-by-hop to generate an intermediate relation path. We conduct extensive experiments on two widely used multi-hop KBQA datasets to prove the effectiveness of our model.","doi":"10.24963/ijcai.2020/500","url":"https://ijcai.org/proceedings/2020/500","pdf":"https://ijcai.org/proceedings/2020/0500.pdf","markdown":"https://ijcai.org/proceedings/2020/500.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/500","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Natural Language Processing","track":"Main track","number":500,"pages":{"first":3615,"last":3621},"keywords":["Natural Language Processing: Question Answering"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24709/file_24709.m3u8","length_sec":290,"provider":"hls"}],"references":[{"position":1,"raw":"[Bai et al., 2019] Song Bai, Feihu Zhang, and Philip H. 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