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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>To meet the challenge of incompleteness within Knowledge Graphs, Knowledge Graph Embedding (KGE) has emerged as the fundamental methodology for predicting the missing link (Link Prediction), by mapping entities and relations as low-dimensional vectors in continuous space. However, current KGE models often struggle with the polysemy issue, where entities exhibit different semantic characteristics depending on the relations in which they participate. Such limitation stems from weak interactions between entities and their relation contexts, leading to low expressiveness in modeling complex structures and resulting in inaccurate predictions. To address this, we propose Contextualized Quaternion Embedding (ConQuatE), a model that enhances the representation learning of entities across multiple semantic dimensions by leveraging quaternion rotation to capture diverse relational contexts. In specific, ConQuatE incorporates contextual cues from various connected relations to enrich the original entity representations. Notably, this is achieved through efficient vector transformations in quaternion space, without any extra information required other than original triples. Experimental results demonstrate that our model outperforms state-of-the-art models for Link Prediction on four widely recognized datasets: FB15k-237, WN18RR, FB15k, and WN18.<\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3714411","type":"journal-article","created":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T09:09:43Z","timestamp":1739005783000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Contextualized Quaternion Embedding Towards Polysemy in Knowledge Graph for Link Prediction"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6474-9238","authenticated-orcid":false,"given":"Jie","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8277-281X","authenticated-orcid":false,"given":"Yinlong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7647-3603","authenticated-orcid":false,"given":"Shu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3607-0022","authenticated-orcid":false,"given":"Peng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5678-3038","authenticated-orcid":false,"given":"Yanping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,23]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3643806"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3671148"},{"key":"e_1_3_3_4_2","first-page":"2787","volume-title":"Neural Information Processing Systems","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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