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Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>\n            Recently, with the development of temporal knowledge graph technology, more and more Temporal Knowledge Graph Embedded (TKGE) models have been developed. The effectiveness of TKGE largely depends on the ability to model intrinsic relation patterns and capture specific information about entities and relations. However, existing approaches can capture only some of them with insufficient modeling capacity, and none has a \u201cdeep\u201d architecture for modeling the entries in a quadruple at the same dimension. In this article, we propose a more powerful KGE framework named\n            <jats:bold>DuCape<\/jats:bold>\n            , which combines a dual quaternion and capsule network in modeling for the first time to make up for the defects of existing TKGE models. In dual quaternion vector space, the head entity learns a\n            <jats:italic>k<\/jats:italic>\n            -dimensional rigid transformation parametrized by relation and time, falling near its corresponding tail entity. Further, we employ the embeddings of entities, relations, and time trained from dual quaternion vector space as the input to capsule networks. Experimental results on several basic datasets show that the DuCape model constructed in this article is superior to existing state-of-the-art models.\n          <\/jats:p>","DOI":"10.1145\/3589644","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T11:59:23Z","timestamp":1680004763000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["DuCape: Dual Quaternion and Capsule Network\u2013Based Temporal Knowledge Graph Embedding"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0449-4699","authenticated-orcid":false,"given":"Sensen","family":"Zhang","sequence":"first","affiliation":[{"name":"Renmin University of China, Haidian Qu, Beijing Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3431-5954","authenticated-orcid":false,"given":"Xun","family":"Liang","sequence":"additional","affiliation":[{"name":"Renmin University of China, Haidian Qu, Beijing Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4126-9770","authenticated-orcid":false,"given":"Hui","family":"Tang","sequence":"additional","affiliation":[{"name":"Renmin University of China, Haidian Qu, Beijing Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8653-6225","authenticated-orcid":false,"given":"Xiangping","family":"Zheng","sequence":"additional","affiliation":[{"name":"Renmin University of China, Haidian Qu, Beijing Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4449-4047","authenticated-orcid":false,"given":"Alex","family":"X. Zhang","sequence":"additional","affiliation":[{"name":"Renmin University of China, Haidian Qu, Beijing Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5245-8510","authenticated-orcid":false,"given":"Yuefeng","family":"Ma","sequence":"additional","affiliation":[{"name":"Qufu Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376746"},{"key":"e_1_3_2_3_2","first-page":"2787","volume-title":"Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting held December 5\u20138, 2013, Lake Tahoe, NV","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garc\u00eda-Dur\u00e1n, Jason Weston, and Oksana Yakhnenko. 2013. 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