{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:01:42Z","timestamp":1742986902651,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819772346"},{"type":"electronic","value":"9789819772353"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-7235-3_17","type":"book-chapter","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T17:02:38Z","timestamp":1724778158000},"page":"256-271","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EvoREG: Evolutional Modeling with\u00a0Relation-Entity Dual-Guidance for\u00a0Temporal Knowledge Graph Reasoning"],"prefix":"10.1007","author":[{"given":"Peiheng","family":"He","sequence":"first","affiliation":[]},{"given":"Yingjie","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Chengxin","family":"He","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Duan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"issue":"1","key":"17_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","volume":"2","author":"J Bollen","year":"2011","unstructured":"Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1\u20138 (2011)","journal-title":"J. Comput. Sci."},{"key":"17_CR2","unstructured":"Bordes, A., Usunier, N., Garc\u00eda-Dur\u00e1n, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787\u20132795 (2013)"},{"key":"17_CR3","first-page":"2","volume":"12","author":"E Boschee","year":"2015","unstructured":"Boschee, E., Lautenschlager, J., O\u2019Brien, S., Shellman, S., Starz, J., Ward, M.: Icews coded event data. Harvard Dataverse 12, 2 (2015)","journal-title":"Harvard Dataverse"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: AAAI, pp. 1811\u20131818 (2018)","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"17_CR5","unstructured":"Gao, H., Ji, S.: Graph u-nets. In: ICML, pp. 2083\u20132092 (2019)"},{"issue":"5","key":"17_CR6","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.3390\/rs14051214","volume":"14","author":"X Ge","year":"2022","unstructured":"Ge, X., et al.: Disaster prediction knowledge graph based on multi-source spatio-temporal information. Remote Sensing 14(5), 1214 (2022)","journal-title":"Remote Sensing"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Geng, Y., et al.: Relational message passing for fully inductive knowledge graph completion. In: ICDE, pp. 1221\u20131233 (2023)","DOI":"10.1109\/ICDE55515.2023.00098"},{"key":"17_CR8","unstructured":"Han, Z., Chen, P., Ma, Y., Tresp, V.: Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In: ICLR (2021)"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Han, Z., Ding, Z., Ma, Y., Gu, Y., Tresp, V.: Learning neural ordinary equations for forecasting future links on temporal knowledge graphs. In: EMNLP (1) (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.658"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Jin, W., Qu, M., Jin, X., Ren, X.: Recurrent event network: autoregressive structure inferenceover temporal knowledge graphs. In: EMNLP, pp. 6669\u20136683 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.541"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Li, Y., Sun, S., Zhao, J.: TiRGN: time-Guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning. In: IJCAI, pp. 2152\u20132158 (2022)","DOI":"10.24963\/ijcai.2022\/299"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Complex evolutional pattern learning for temporal knowledge graph reasoning. In: ACL, pp. 290\u2013296 (2022)","DOI":"10.18653\/v1\/2022.acl-short.32"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: HiSMatch: historical structure matching based temporal knowledge graph reasoning. In: EMNLP, pp. 7328\u20137338 (2022)","DOI":"10.18653\/v1\/2022.findings-emnlp.542"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Temporal knowledge graph reasoning based on evolutional representation learning. In: SIGIR, pp. 408\u2013417 (2021)","DOI":"10.1145\/3404835.3462963"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Liang, K., et al.: Learn from relational correlations and periodic events for temporal knowledge graph reasoning. In: SIGIR, pp. 1559\u20131568 (2023)","DOI":"10.1145\/3539618.3591711"},{"key":"17_CR16","unstructured":"Liang, K., et al.: Reasoning over different types of knowledge graphs: Static, temporal and multi-modal. arXiv preprint arXiv:2212.05767 (2022)"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Liu, K., Zhao, F., Xu, G., Wang, X., Jin, H.: RETIA: relation-Entity twin-interact aggregation for temporal knowledge graph extrapolation. In: ICDE, pp. 1761\u20131774 (2023)","DOI":"10.1109\/ICDE55515.2023.00138"},{"key":"17_CR18","unstructured":"Van\u00a0der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Pareja, A., et al.: EvolveGCN: evolving graph convolutional networks for dynamic graphs. In: AAAI, pp. 5363\u20135370 (2020)","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M.S., Kipf, T.N., Bloem, P., van\u00a0den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: ESWC, pp. 593\u2013607 (2018)","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B.: End-to-end structure-aware convolutional networks for knowledge base completion. In: AAAI, pp. 3060\u20133067 (2019)","DOI":"10.1609\/aaai.v33i01.33013060"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Sun, H., Zhong, J., Ma, Y., Han, Z., He, K.: TimeTraveler: reinforcement learning for temporal knowledge graph forecasting. In: EMNLP (1), pp. 8306\u20138319 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.655"},{"key":"17_CR23","unstructured":"Sun, Z., Deng, Z., Nie, J., Tang, J.: RotatE: knowledge graph embedding by relational rotation in complex space. In: ICLR (2019)"},{"key":"17_CR24","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, pp. 2071\u20132080 (2016)"},{"key":"17_CR25","unstructured":"Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.P.: Composition-based multi-relational graph convolutional networks. In: ICLR (2020)"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp. 1112\u20131119 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"17_CR27","unstructured":"Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: ICLR (2015)"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, M., Xia, Y., Liu, Q., Wu, S., Wang, L.: Learning long- and short-term representations for temporal knowledge graph reasoning. In: WWW, pp. 2412\u20132422 (2023)","DOI":"10.1145\/3543507.3583242"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, C., Chen, M., Fan, C., Cheng, G., Zhang, Y.: Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks. In: AAAI, pp. 4732\u20134740 (2021)","DOI":"10.1609\/aaai.v35i5.16604"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-7235-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T17:16:49Z","timestamp":1724779009000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-7235-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819772346","9789819772353"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-7235-3_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jinhua","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb2024.zjnu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}