{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T18:31:51Z","timestamp":1754159511366,"version":"3.41.2"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"5","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Knowledge Graph Completion (KGC) aims to complete KGs by predicting missing entities. A common solution for KGC is Knowledge Graph Embedding (KGE), which assumes that semantical similar entities or relationships should possess similar representations in high-dimensional space. In KGE, a heuristic score function of the head entity and its relation with different operators is required. A typical technique is regularization for tensor factorization, such as the Nuclear-p norm and the Frobenius norm of the query\/entity embedding, which significantly improve the KGE model performance on the KGC task. However, the\n            <jats:italic toggle=\"yes\">Co-Relation<\/jats:italic>\n            s, including the association between tail entities (\n            <jats:italic toggle=\"yes\">Co-Query Relation<\/jats:italic>\n            ) and the association between queries (\n            <jats:italic toggle=\"yes\">Co-Entity Relation<\/jats:italic>\n            ), desirable for KGC are not fully considered in existing embedding regularization techniques. In this article, we theoretically interpret the role of Co-Relation in KGE and propose a novel\n            <jats:italic toggle=\"yes\">ConR<\/jats:italic>\n            regularization approach to learn embedding that takes Co-Relations into account. Extensive experiments show that our model improves static and temporal KGC tasks over decomposition-based models, ComplEx and TuckER. Further analysis of the score cumulative distribution function and embedding visualization demonstrates the effectiveness of\u00a0\n            <jats:italic toggle=\"yes\">ConR<\/jats:italic>\n            .\n          <\/jats:p>","DOI":"10.1145\/3733231","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T12:55:32Z","timestamp":1745931332000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Including Co-Relation via Concatenate Operator for Static and Temporal Knowledge Graph Embedding"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1346-1282","authenticated-orcid":false,"given":"Likang","family":"Xiao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Complex &amp; Critical Software Environment, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-0300","authenticated-orcid":false,"given":"Richong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6807-0089","authenticated-orcid":false,"given":"Junfan","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Complex &amp; Critical Software Environment, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7875-3236","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"TravelSky Technology Limited, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1007\/978-3-540-76298-0_52","volume-title":"The Semantic Web","author":"Auer S\u00f6ren","year":"2007","unstructured":"S\u00f6ren Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007. 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Retrieved from http:\/\/proceedings.mlr.press\/v48\/trouillon16.html"},{"key":"e_1_3_1_37_2","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey E. Hinton. 2008. Visualizing data using t-SNE. J Mach. Learn. Res. 9 (2008), 2579\u20132605.","journal-title":"J Mach. Learn. Res"},{"key":"e_1_3_1_38_2","volume-title":"Proceedings of the 8th International Conference on Learning Representations (ICLR \u201920)","author":"Vashishth Shikhar","year":"2020","unstructured":"Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Talukdar. 2020. Composition-based multi-relational graph convolutional networks. In Proceedings of the 8th International Conference on Learning Representations (ICLR \u201920). OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=BylA_C4tPr"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/2629489"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450043"},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1145\/3269206.3271739","volume-title":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","author":"Wang Hongwei","year":"2018","unstructured":"Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 417\u2013426."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2022.ACL-LONG.295"},{"key":"e_1_3_1_43_2","first-page":"9929","volume-title":"Proceedings of the 37th International Conference on Machine Learning (ICML \u201920)","volume":"119","author":"Wang Tongzhou","year":"2020","unstructured":"Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In Proceedings of the 37th International Conference on Machine Learning (ICML \u201920), Vol. 119. PMLR, 9929\u20139939. Retrieved from http:\/\/proceedings.mlr.press\/v119\/wang20k.html"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2020.EMNLP-MAIN.462"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.18653\/V1\/2023.FINDINGS-ACL.458"},{"key":"e_1_3_1_46_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations (ICLR \u201915)","author":"Yang Bishan","year":"2015","unstructured":"Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding entities and relations for learning and inference in knowledge bases. In Proceedings of the 3rd International Conference on Learning Representations (ICLR \u201915). Yoshua Bengio and Yann LeCun (Eds.), OpenReview.net. Retrieved from http:\/\/arxiv.org\/abs\/1412.6575"},{"key":"e_1_3_1_47_2","unstructured":"Liang Yao Chengsheng Mao and Yuan Luo. 2019. KG-BERT: BERT for knowledge graph completion. arXiv:1909.03193. 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