{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:55:54Z","timestamp":1777704954394,"version":"3.51.4"},"reference-count":36,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2022,11,11]]},"abstract":"<jats:p>\u00a0Many real-world knowledge graphs are complex and keep evolving over time. Inferring missing facts in temporal knowledge graphs is a fundamental and challenging task. Previous studies focus on link prediction in static knowledge graphs which hardly extracts the temporal features effectively. In this paper, we propose a novel deep learning model, namely KBGAT-BiLSTM, which is capable of solving long-term predict problems and is suitable for temporal knowledge graph with complex structures. First, we adapt the Graph Attention Network (GAT) to learn the structural features of knowledge graph. Then we utilize the Bidirectional Long Short-Term Memory Networks (BiLSTM) to learn the temporal features and obtain the low-dimensional embeddings of entities and relations. Finally, we employ a scoring function for link prediction in temporal knowledge graphs. Through extensive experiments on YAGO, WIKI, and ICEWS18 datasets, we demonstrate the effectiveness of our model, compare the performance of our model with several different state-of-the-art methods and further analyze the properties of the proposed method.<\/jats:p>","DOI":"10.3233\/jifs-210943","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T11:12:17Z","timestamp":1661512337000},"page":"7983-7994","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning neighborhood-based embedding sequence for link prediction in temporal knowledge graphs"],"prefix":"10.1177","volume":"43","author":[{"given":"Liqin","family":"Wang","sequence":"first","affiliation":[{"name":"Hebei University of Technology","place":["China"]},{"name":"Hebei Province Key Laboratory of Big Data Computing","place":["China"]},{"name":"Hebei Engineering Research Center of Data-Driven Industrial Intelligent","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Chu","sequence":"additional","affiliation":[{"name":"Hebei University of Technology","place":["China"]},{"name":"Hebei Province Key Laboratory of Big Data Computing","place":["China"]},{"name":"Hebei Engineering Research Center of Data-Driven Industrial Intelligent","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfeng","family":"Dong","sequence":"additional","affiliation":[{"name":"Hebei University of Technology","place":["China"]},{"name":"Hebei Province Key Laboratory of Big Data Computing","place":["China"]},{"name":"Hebei Engineering Research Center of Data-Driven Industrial Intelligent","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enhai","family":"Liu","sequence":"additional","affiliation":[{"name":"Hebei University of Technology","place":["China"]},{"name":"Hebei Province Key Laboratory of Big Data Computing","place":["China"]},{"name":"Hebei Engineering Research Center of Data-Driven Industrial Intelligent","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linhao","family":"Li","sequence":"additional","affiliation":[{"name":"Hebei University of Technology","place":["China"]},{"name":"Hebei Province Key Laboratory of Big Data Computing","place":["China"]},{"name":"Hebei Engineering Research Center of Data-Driven Industrial Intelligent","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","first-page":"2018","article-title":"Adversarial network embedding","volume":"32","author":"Dai Q.","unstructured":"DaiQ., LiQ., TangJ. et al., Adversarial network embedding, Proceedings of the AAAI Conference on Artificial Intelligence 32(1), 2018.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"HamidiM. 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