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Process."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>\n            Multi-hop Knowledge Graph Question Answering aims at finding an entity to answer natural language questions from knowledge graphs. When humans perform multi-hop reasoning, people tend to focus on specific relations across different hops and confirm the next entity. Therefore, most algorithms choose the wrong specific relation, which makes the system deviate from the correct reasoning path. The specific relation at each hop plays an important role in multi-hop question answering. Existing work mainly relies on the question representation as relation information, which cannot accurately calculate the specific relation distribution. In this article, we propose an interpretable assistance framework that fully utilizes the relation embeddings to assist in calculating relation distributions at each hop. Moreover, we employ the fusion attention mechanism to ensure the integrity of relation information and hence to enrich the relation embeddings. The experimental results on three English datasets and one Chinese dataset demonstrate that our method significantly outperforms all baselines. The source code of REAN will be available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/2399240664\/REAN\">https:\/\/github.com\/2399240664\/REAN<\/jats:ext-link>\n          <\/jats:p>","DOI":"10.1145\/3635114","type":"journal-article","created":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T06:07:44Z","timestamp":1702706864000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Relation Embedding Assistance Networks for Multi-hop Question Answering"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0912-8257","authenticated-orcid":false,"given":"Songlin","family":"Jiao","sequence":"first","affiliation":[{"name":"Shandong Jiao Tong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7217-3109","authenticated-orcid":false,"given":"Zhenfang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shandong Jiao Tong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4529-2695","authenticated-orcid":false,"given":"Jiangtao","family":"Qi","sequence":"additional","affiliation":[{"name":"Shandong Jiao Tong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8010-8190","authenticated-orcid":false,"given":"Fuyong","family":"Xu","sequence":"additional","affiliation":[{"name":"Shandong Normal University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9126-3799","authenticated-orcid":false,"given":"Hongli","family":"Pei","sequence":"additional","affiliation":[{"name":"Shandong Jiao Tong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2154-0984","authenticated-orcid":false,"given":"Wenling","family":"Wang","sequence":"additional","affiliation":[{"name":"Lu Dong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5464-1687","authenticated-orcid":false,"given":"Ze","family":"Song","sequence":"additional","affiliation":[{"name":"Shandong Jiao Tong University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2905-5473","authenticated-orcid":false,"given":"Peiyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Shandong Normal University, China"}]}],"member":"320","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1533","volume-title":"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing","author":"Berant Jonathan","year":"2013","unstructured":"Jonathan Berant , Andrew Chou , Roy Frostig , and Percy Liang . 2013. Semantic parsing on freebase from question-answer pairs. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing . 1533\u20131544."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376746"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07965-0"},{"key":"e_1_3_1_5_2","unstructured":"Jifan Chen Shih-Ting Lin and Greg Durrett . 2019. Multi-hop question answering via reasoning chains. arXiv preprint arXiv:1910.02610."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-demo.39"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/519"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.INS.2022.11.042"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1026"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/500"},{"key":"e_1_3_1_12_2","unstructured":"Xianpei Han Zhichun Wang Jiangtao Zhang Qinghua Wen Wenqi Li Buzhou Tang Qi Wang Zhifan Feng Yang Zhang Yajuan Lu Haitao Wang Wenliang Chen Hao Shao Yubo Chen Kang Liu Jun Zhao Taifeng Wang Kezun Zhang Meng Wang Yinlin Jiang Guilin Qi Lei Zou Sen Hu Minhao Zhang and Yinnian Lin. 2020. Overview of the CCKS 2019 knowledge graph evaluation track: entity relation event and QA. arXiv:abs\/2003.03875 (2020). Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:212633633"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1021"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441753"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-eacl.87"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290956"},{"key":"e_1_3_1_17_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations, ICLR 2017","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations, ICLR 2017 . OpenReview.net. 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