{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T23:46:05Z","timestamp":1769816765856,"version":"3.49.0"},"reference-count":14,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,4,28]]},"abstract":"<jats:p>Knowledge Graph (KG) embedding approaches have been proved effective to infer new facts for a KG based on the existing ones\u2013a problem known as KG completion. However, most of them have focused on static KGs, in fact, relational facts in KGs often show temporal dynamics, e.g., the fact (US, has president, Barack Obama, [2009\u20132017]) is only valid from 2009 to 2017. Therefore, utilizing available time information to develop temporal KG embedding models is an increasingly important problem. In this paper, we propose a new hyperplane-based time-aware KG embedding model for temporal KG completion. By employing the method of time-specific hyperplanes, our model could explicitly incorporate time information in the entity-relation space to predict missing elements in the KG more effectively, especially temporal scopes for facts with missing time information. Moreover, in order to model and infer four important relation patterns including symmetry, antisymmetry, inversion and composition, we map facts happened at the same time into a polar coordinate system. During training procedure, a time-enhanced negative sampling strategy is proposed to get more effective negative samples. Experimental results on datasets extracted from real-world temporal KGs show that our model significantly outperforms existing state-of-the-art approaches for the KG completion task.<\/jats:p>","DOI":"10.3233\/jifs-211950","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T14:11:27Z","timestamp":1638886287000},"page":"5457-5469","source":"Crossref","is-referenced-by-count":4,"title":["Hyperplane-based time-aware knowledge graph embedding for temporal knowledge graph completion"],"prefix":"10.1177","volume":"42","author":[{"given":"Peng","family":"He","sequence":"first","affiliation":[{"name":"Information Engineering University, China"},{"name":"Zhengzhou University of Technology, China"}]},{"given":"Gang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Information Engineering University, China"}]},{"given":"Hongbo","family":"Liu","sequence":"additional","affiliation":[{"name":"Information Engineering University, China"}]},{"given":"Yi","family":"Xia","sequence":"additional","affiliation":[{"name":"Information Engineering University, China"}]},{"given":"Ling","family":"Wang","sequence":"additional","affiliation":[{"name":"Information Engineering University, China"}]}],"member":"179","reference":[{"issue":"11","key":"10.3233\/JIFS-211950_ref3","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"Wordnet: a lexical database for english","volume":"38","author":"Miller","year":"1995","journal-title":"Communications of the ACM"},{"key":"10.3233\/JIFS-211950_ref8","first-page":"33","article-title":"A review of relationalmachine learning for knowledge graphs, }(1) 11\u2013","volume":"104","author":"Nickel","year":"2015","journal-title":"Proceedings of theIEEE"},{"key":"10.3233\/JIFS-211950_ref9","first-page":"2743","article-title":"Knowledge graph embedding: Asurvey of approaches and applications, (12) 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279\u2013311.","DOI":"10.1007\/BF02289464"},{"key":"10.3233\/JIFS-211950_ref41","doi-asserted-by":"crossref","first-page":"4054","DOI":"10.1609\/aaai.v35i5.16526","article-title":"Neural latent space model for dynamic networks and temporalknowledge graphs","volume":"35","author":"Gracious","year":"2021","journal-title":"Proceedings of the AAAI Conference onArtificial Intelligence"},{"key":"10.3233\/JIFS-211950_ref42","doi-asserted-by":"crossref","first-page":"4732","DOI":"10.1609\/aaai.v35i5.16604","article-title":"Learning fromhistory: Modeling temporal knowledge graphs with sequentialcopy-generation networks","volume":"35","author":"Zhu","year":"2021","journal-title":"Proceedings of the AAAIConference on Artificial Intelligence"},{"issue":"3","key":"10.3233\/JIFS-211950_ref44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3443687","article-title":"Tpmod: A tendency-guidedprediction model for temporal knowledge graph 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