{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T06:33:44Z","timestamp":1761806024391,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032095268","type":"print"},{"value":"9783032095275","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-09527-5_22","type":"book-chapter","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T06:28:44Z","timestamp":1761805724000},"page":"407-424","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Query-Aware Dynamic Representation Learning for\u00a0Temporal Knowledge Graph Reasoning"],"prefix":"10.1007","author":[{"given":"Juan","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanbo J.","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinchao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanzhuo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"22_CR1","unstructured":"Achiam, J., et\u00a0al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"22_CR2","doi-asserted-by":"publisher","unstructured":"Boschee, E., Lautenschlager, J., O\u2019Brien, S., Shellman, S., Starz, J., Ward, M.: ICEWS coded event data (2015). https:\/\/doi.org\/10.7910\/DVN\/28075","DOI":"10.7910\/DVN\/28075"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Chen, B., Xiao, C., Zhou, F.: Natural evolution-based dual-level aggregation for temporal knowledge graph reasoning. In: Al-Onaizan, Y., Bansal, M., Chen, Y.N. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, Miami, Florida, USA, November 2024. https:\/\/aclanthology.org\/2024.findings-emnlp.543\/","DOI":"10.18653\/v1\/2024.findings-emnlp.543"},{"key":"22_CR4","unstructured":"Chen, W., Wan, H., Wu, Y., Zhao, S., Cheng, J., Li, Y., Lin, Y.: Local-global history-aware contrastive learning for temporal knowledge graph reasoning (2023). https:\/\/arxiv.org\/abs\/2312.01601"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merri\u00ebnboer, B., Gulcehre, \u00c7., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Dasgupta, S.S., Ray, S.N., Talukdar, P.: Hyte: hyperplane-based temporally aware knowledge graph embedding. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2001\u20132011 (2018)","DOI":"10.18653\/v1\/D18-1225"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"22_CR8","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2019)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Gao, Y., He, Y., Kan, Z., Han, Y., Qiao, L., Li, D.: Learning joint structural and temporal contextualized knowledge embeddings for temporal knowledge graph completion. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 417\u2013430 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.28"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Dur\u00e1n, A., Duman\u010di\u0107, S., Niepert, M.: Learning sequence encoders for temporal knowledge graph completion. arXiv preprint arXiv:1809.03202 (2018)","DOI":"10.18653\/v1\/D18-1516"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"issue":"8","key":"22_CR12","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Jin, W., Qu, M., Jin, X., Ren, X.: Recurrent event network: autoregressive structure inference over temporal knowledge graphs (2020). https:\/\/arxiv.org\/abs\/1904.05530","DOI":"10.18653\/v1\/2020.emnlp-main.541"},{"key":"22_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"22_CR15","unstructured":"Lacroix, T., Obozinski, G., Usunier, N.: Tensor decompositions for temporal knowledge base completion. arXiv preprint arXiv:2004.04926 (2020)"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Leblay, J., Chekol, M.W.: Deriving validity time in knowledge graph. In: Companion Proceedings of the the Web Conference 2018, pp. 1771\u20131776 (2018)","DOI":"10.1145\/3184558.3191639"},{"key":"22_CR17","unstructured":"Leetaru, K., Schrodt, P.A.: Gdelt: Global data on events, location, and tone, 1979\u20132012. In: ISA Annual Convention, vol.\u00a02, pp. 1\u201349. Citeseer (2013)"},{"key":"22_CR18","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: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pp. 2152\u20132158 (2022)","DOI":"10.24963\/ijcai.2022\/299"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Complex evolutional pattern learning for temporal knowledge graph reasoning. arXiv preprint arXiv:2203.07782 (2022)","DOI":"10.18653\/v1\/2022.acl-short.32"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Li, Z., Hou, Z., Guan, S., Jin, X., Peng, W., Bai, L., Lyu, Y., Li, W., Guo, J., Cheng, X.: Hismatch: Historical structure matching based temporal knowledge graph reasoning (2022). https:\/\/arxiv.org\/abs\/2210.09708","DOI":"10.18653\/v1\/2022.findings-emnlp.542"},{"key":"22_CR21","unstructured":"Li, Z., et al.: Temporal knowledge graph reasoning based on evolutional representation learning (2021). https:\/\/arxiv.org\/abs\/2104.10353"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Liang, K., et al.: Mgksite: multi-modal knowledge-driven site selection via intra and inter-modal graph fusion. IEEE Trans. Multimed. (2024)","DOI":"10.1109\/TMM.2024.3521742"},{"key":"22_CR23","doi-asserted-by":"publisher","unstructured":"Liang, K., Meng, L., Li, H., Wang, J., Lan, L., Li, M., Liu, X., Wang, H.: From concrete to abstract: multi-view clustering on relational knowledge. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201318 (2025). https:\/\/doi.org\/10.1109\/TPAMI.2025.3582689","DOI":"10.1109\/TPAMI.2025.3582689"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Liang, K.,et al.: Learn from relational correlations and periodic events for temporal knowledge graph reasoning. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1559\u20131568 (2023)","DOI":"10.1145\/3539618.3591711"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Liao, R., Jia, X., Li, Y., Ma, Y., Tresp, V.: Gentkg: Generative forecasting on temporal knowledge graph with large language models. arXiv preprint arXiv:2310.07793 (2023)","DOI":"10.18653\/v1\/2024.findings-naacl.268"},{"key":"22_CR26","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: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp. 1761\u20131774. IEEE (2023)","DOI":"10.1109\/ICDE55515.2023.00138"},{"key":"22_CR27","unstructured":"Mahdisoltani, F., Biega, J., Suchanek, F.M.: Yago3: A knowledge base from multilingual wikipedias. In: CIDR (2013)"},{"key":"22_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"The Semantic Web","author":"M Schlichtkrull","year":"2018","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., van\u00a0den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593\u2013607. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38"},{"key":"22_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1007\/978-3-030-04167-0_33","volume-title":"Neural Information Processing","author":"Y Seo","year":"2018","unstructured":"Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 362\u2013373. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04167-0_33"},{"key":"22_CR30","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: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 3060\u20133067 (2019)","DOI":"10.1609\/aaai.v33i01.33013060"},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"Sun, H., Zhong, J., Ma, Y., Han, Z., He, K.: Timetraveler: reinforcement learning for temporal knowledge graph forecasting. arXiv preprint arXiv:2109.04101 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.655"},{"key":"22_CR32","unstructured":"Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)"},{"key":"22_CR33","doi-asserted-by":"publisher","unstructured":"Tao, S., Cao, Q., Shen, H., Huang, J., Wu, Y., Cheng, X.: Single node injection attack against graph neural networks. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, CIKM \u201921, pp. 1794\u20131803. ACM, October 2021. https:\/\/doi.org\/10.1145\/3459637.3482393","DOI":"10.1145\/3459637.3482393"},{"key":"22_CR34","unstructured":"Team, Q.: Qwq-32b: embracing the power of reinforcement learning, March 2025. https:\/\/qwenlm.github.io\/blog\/qwq-32b\/"},{"key":"22_CR35","unstructured":"Touvron, H., et\u00a0al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"22_CR36","unstructured":"Vaswani, A., et al.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"key":"22_CR37","first-page":"8384","volume":"37","author":"J Wang","year":"2024","unstructured":"Wang, J., et al.: Large language models-guided dynamic adaptation for temporal knowledge graph reasoning. Adv. Neural. Inf. Process. Syst. 37, 8384\u20138410 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"22_CR38","doi-asserted-by":"crossref","unstructured":"Xia, Y., Wang, D., Liu, Q., Wang, L., Wu, S., Zhang, X.: Chain-of-history reasoning for temporal knowledge graph forecasting. arXiv preprint arXiv:2402.14382 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.955"},{"key":"22_CR39","doi-asserted-by":"publisher","unstructured":"Xiang, S., Cheng, D., Shang, C., Zhang, Y., Liang, Y.: Temporal and heterogeneous graph neural network for financial time series prediction. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM \u201922. ACM, October 2022. https:\/\/doi.org\/10.1145\/3511808.3557089","DOI":"10.1145\/3511808.3557089"},{"key":"22_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, M., Xia, Y., Liu, Q., Wu, S., Wang, L.: Learning latent relations for temporal knowledge graph reasoning. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 12617\u201312631 (2023)","DOI":"10.18653\/v1\/2023.acl-long.705"},{"key":"22_CR41","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: Proceedings of the ACM Web Conference 2023, pp. 2412\u20132422 (2023)","DOI":"10.1145\/3543507.3583242"},{"key":"22_CR42","doi-asserted-by":"publisher","unstructured":"Zhang, S., Wei, W., Huang, R., Xie, W., Chen, D.: Modeling historical relevant and local frequency context for representation-based temporal knowledge graph forecasting. In: Al-Onaizan, Y., Bansal, M., Chen, Y.N. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 7675\u20137686. Association for Computational Linguistics, Miami, Florida, USA, November 2024. https:\/\/doi.org\/10.18653\/v1\/2024.findings-emnlp.451","DOI":"10.18653\/v1\/2024.findings-emnlp.451"},{"key":"22_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, W., Peng, L., Ge, X., Yang, L., Chen, L., Li, W.: Spatio-temporal knowledge graph-based research on agro-meteorological disaster monitoring. Remote Sensing 15(18) (2023)","DOI":"10.3390\/rs15184403"},{"key":"22_CR44","unstructured":"Zhu, C., Chen, M., Fan, C., Cheng, G., Zhan, Y.: Learning from history: modeling temporal knowledge graphs with sequential copy-generation networks (2021). https:\/\/arxiv.org\/abs\/2012.08492"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09527-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T06:29:10Z","timestamp":1761805750000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09527-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,29]]},"ISBN":["9783032095268","9783032095275"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09527-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,29]]},"assertion":[{"value":"29 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Source code and data can be found at:\n                      \n                      .","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Supplemental Material Statement"}},{"value":"ISWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nara","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2025.semanticweb.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}