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Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>\n            Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the\n            <jats:italic>Goodness<\/jats:italic>\n            of relations and the\n            <jats:italic>Closeness<\/jats:italic>\n            of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.\n          <\/jats:p>","DOI":"10.1145\/3443687","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T15:42:54Z","timestamp":1619019774000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9546-3208","authenticated-orcid":false,"given":"Luyi","family":"Bai","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangnan","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingcheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenting","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Northeastern University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3191526"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the International Conference on Machine Learning. 2397\u20132406","author":"Xiong Caiming","year":"2016","unstructured":"Caiming Xiong , Stephen Merity , and Richard Scoher . 2016 . Dynamic memory networks for visual and textual question answering . In Proceedings of the International Conference on Machine Learning. 2397\u20132406 . Caiming Xiong, Stephen Merity, and Richard Scoher. 2016. Dynamic memory networks for visual and textual question answering. In Proceedings of the International Conference on Machine Learning. 2397\u20132406."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Xiong Caiming","year":"2017","unstructured":"Caiming Xiong , Victor Zhong , and Richaid Socher . 2017 . Dynamic coattention networks for question answering . In Proceedings of the International Conference on Learning Representations. Caiming Xiong, Victor Zhong, and Richaid Socher. 2017. Dynamic coattention networks for question answering. 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