{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T08:04:39Z","timestamp":1790064279863,"version":"4.0.1"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031778438","type":"print"},{"value":"9783031778445","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-77844-5_13","type":"book-chapter","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:06:03Z","timestamp":1732579563000},"page":"237-255","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Distilling Event Sequence Knowledge From Large Language Models"],"prefix":"10.1007","author":[{"given":"Somin","family":"Wadhwa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oktie","family":"Hassanzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debarun","family":"Bhattacharjya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ken","family":"Barker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Agrawal, S., Zhou, C., Lewis, M., Zettlemoyer, L., Ghazvininejad, M.: In-context examples selection for machine translation. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 8857\u20138873. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.564","DOI":"10.18653\/v1\/2023.findings-acl.564"},{"key":"13_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-0933-2","volume-title":"Topic Detection and Tracking: Event-Based Information Organization","author":"J Allan","year":"2012","unstructured":"Allan, J.: New York, 1st edn. Springer (2012). https:\/\/doi.org\/10.1007\/978-1-4615-0933-2","edition":"1"},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Allan, J., Carbonell, J.G., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study final report. In: Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, February, 1998 (1998). https:\/\/doi.org\/10.1184\/R1\/6626252.v1","DOI":"10.1184\/R1\/6626252.v1"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"An, S., et al.: How do in-context examples affect compositional generalization? In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Volume 1, Long Papers, pp. 11027\u201311052. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.618","DOI":"10.18653\/v1\/2023.acl-long.618"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Begleiter, R., El-Yaniv, R., Yona, G.: On prediction using variable order Markov models. J. Artif. Intell. Res. 22, 385\u2013421 (2004). https:\/\/doi.org\/10.1613\/jair.1491","DOI":"10.1613\/jair.1491"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Bhattacharjya, D., Sihag, S., Hassanzadeh, O., Bialik, L.: Summary Markov models for event sequences. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 4836\u20134842. International Joint Conferences on Artificial Intelligence Organization (2022). https:\/\/doi.org\/10.24963\/ijcai.2022\/670","DOI":"10.24963\/ijcai.2022\/670"},{"key":"13_CR7","unstructured":"Brown, T., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 1877\u20131901. Curran Associates, Inc. (2020). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Cekinel, R.F., Karagoz, P.: Event prediction from news text using subgraph embedding and graph sequence mining. World Wide Web 25, 2403\u20132428 (2022). https:\/\/doi.org\/10.1007\/s11280-021-01002-1","DOI":"10.1007\/s11280-021-01002-1"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Chen, M., et al.: Event-centric natural language processing. In: ACL (2021)","DOI":"10.18653\/v1\/2021.acl-tutorials.2"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Chiang, C.H., Lee, H.y.: Can large language models be an alternative to human evaluations? In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Volume 1, Long Papers, pp. 15607\u201315631. Association for Computational Linguistics, Toronto, Canada (2023).https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.870","DOI":"10.18653\/v1\/2023.acl-long.870"},{"key":"13_CR11","unstructured":"Chung, H.W., et al.: Scaling instruction-finetuned language models (2022)"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1, Long and Short Papers, pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"13_CR13","doi-asserted-by":"publisher","unstructured":"Du, X., et al.: RESIN-11: schema-guided event prediction for 11 newsworthy scenarios. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations, pp. 54\u201363. Association for Computational Linguistics, Seattle, Washington (2022). https:\/\/doi.org\/10.18653\/v1\/2022.naacl-demo.7","DOI":"10.18653\/v1\/2022.naacl-demo.7"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Filatova, E., Hatzivassiloglou, V., McKeown, K.: Automatic creation of domain templates. In: Proceedings of the COLING\/ACL 2006 Main Conference Poster Sessions, pp. 207\u2013214. Association for Computational Linguistics, Sydney, Australia (2006). https:\/\/aclanthology.org\/P06-2027","DOI":"10.3115\/1273073.1273100"},{"issue":"1","key":"13_CR15","first-page":"54","volume":"1","author":"P Fournier-Viger","year":"2017","unstructured":"Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1(1), 54\u201377 (2017)","journal-title":"Data Sci. Pattern Recogn."},{"key":"13_CR16","doi-asserted-by":"publisher","unstructured":"Gholipour\u00a0Ghalandari, D., Ifrim, G.: Examining the state-of-the-art in news timeline summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1322\u20131334. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.122","DOI":"10.18653\/v1\/2020.acl-main.122"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Halawi, D., Zhang, F., Yueh-Han, C., Steinhardt, J.: Approaching human-level forecasting with language models (2024)","DOI":"10.52202\/079017-1598"},{"key":"13_CR18","doi-asserted-by":"publisher","unstructured":"Hassanzadeh, O., et al.: Knowledge-based news event analysis and forecasting toolkit. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pp. 5904\u20135907 (2022). https:\/\/doi.org\/10.24963\/ijcai.2022\/850","DOI":"10.24963\/ijcai.2022\/850"},{"key":"13_CR19","unstructured":"He, X., et al.: AnnoLLM: making large language models to be better crowdsourced annotators (2023)"},{"key":"13_CR20","doi-asserted-by":"publisher","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Hwang, J.D., et al.: COMET-ATOMIC 2020: on symbolic and neural commonsense knowledge graphs. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i7.16792"},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Imani, S., Du, L., Shrivastava, H.: MathPrompter: mathematical reasoning using large language models. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Volume 5: Industry Track, pp. 37\u201342. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-industry.4","DOI":"10.18653\/v1\/2023.acl-industry.4"},{"key":"13_CR23","doi-asserted-by":"publisher","unstructured":"Kamalloo, E., Dziri, N., Clarke, C., Rafiei, D.: Evaluating open-domain question answering in the era of large language models. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, pp. 5591\u20135606. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.307","DOI":"10.18653\/v1\/2023.acl-long.307"},{"key":"13_CR24","doi-asserted-by":"publisher","unstructured":"Li, M., et al.: Connecting the dots: event graph schema induction with path language modeling. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 684\u2013695. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.50","DOI":"10.18653\/v1\/2020.emnlp-main.50"},{"key":"13_CR25","doi-asserted-by":"publisher","unstructured":"Li, S., Zhao, R., Li, M., Ji, H., Callison-Burch, C., Han, J.: Open-domain hierarchical event schema induction by incremental prompting and verification. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Volume 1, Long Papers, pp. 5677\u20135697. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.312","DOI":"10.18653\/v1\/2023.acl-long.312"},{"key":"13_CR26","doi-asserted-by":"publisher","unstructured":"Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43(1) (2010). https:\/\/doi.org\/10.1145\/1824795.1824798","DOI":"10.1145\/1824795.1824798"},{"key":"13_CR27","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1023\/A:1009748302351","volume":"1","author":"H Mannila","year":"1997","unstructured":"Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Disc. 1, 259\u2013289 (1997)","journal-title":"Data Min. Knowl. Disc."},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Mooney, C.H., Roddick, J.F.: Sequential pattern mining \u2013 approaches and algorithms. ACM Comput. Surv. 45(2) (2013). https:\/\/doi.org\/10.1145\/2431211.2431218","DOI":"10.1145\/2431211.2431218"},{"key":"13_CR29","doi-asserted-by":"publisher","unstructured":"Norambuena, B.K., Mitra, T., North, C.: A survey on event-based news narrative extraction. ACM Comput. Surv. 55(14s) (2023). https:\/\/doi.org\/10.1145\/3584741","DOI":"10.1145\/3584741"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Radinsky, K., Davidovich, S., Markovitch, S.: Learning causality for news events prediction. In: Proceedings of the 21st World Wide Web Conference 2012, WWW 2012, Lyon, France, April 16-20, 2012, pp. 909\u2013918. ACM (2012). https:\/\/doi.org\/10.1145\/2187836.2187958","DOI":"10.1145\/2187836.2187958"},{"key":"13_CR31","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1) (2020)"},{"issue":"3","key":"13_CR32","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1111\/j.2517-6161.1985.tb01383.x","volume":"47","author":"A Raftery","year":"1985","unstructured":"Raftery, A.: A model for high-order Markov chains. J. Roy. Stat. Soc. B 47(3), 528\u2013539 (1985)","journal-title":"J. Roy. Stat. Soc. B"},{"issue":"8","key":"13_CR33","doi-asserted-by":"publisher","first-page":"8393","DOI":"10.1007\/s10462-022-10338-7","volume":"56","author":"BS Santana","year":"2023","unstructured":"Santana, B.S., Campos, R., Amorim, E., Jorge, A., Silvano, P., Nunes, S.: A survey on narrative extraction from textual data. Artif. Intell. Rev. 56(8), 8393\u20138435 (2023). https:\/\/doi.org\/10.1007\/s10462-022-10338-7","journal-title":"Artif. Intell. Rev."},{"key":"13_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BFb0014140","volume-title":"Advances in Database Technology \u2014 EDBT \u201996","author":"R Srikant","year":"1996","unstructured":"Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1\u201317. Springer, Heidelberg (1996). https:\/\/doi.org\/10.1007\/BFb0014140"},{"key":"13_CR35","unstructured":"Touvron, H., et\u00a0al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"13_CR36","doi-asserted-by":"publisher","unstructured":"Vrande\u010di\u0107, D., Kr\u00f6tzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78\u201385 (2014). https:\/\/doi.org\/10.1145\/2629489","DOI":"10.1145\/2629489"},{"key":"13_CR37","doi-asserted-by":"publisher","unstructured":"Wadhwa, S., Amir, S., Wallace, B.: Revisiting relation extraction in the era of large language models. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers, pp. 15566\u201315589. Association for Computational Linguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.868","DOI":"10.18653\/v1\/2023.acl-long.868"},{"key":"13_CR38","unstructured":"Wei, J., et al.: Finetuned language models are zero-shot learners. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=gEZrGCozdqR"},{"key":"13_CR39","doi-asserted-by":"publisher","unstructured":"West, P., et al.: Symbolic knowledge distillation: from general language models to commonsense models. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4602\u20134625. Association for Computational Linguistics, Seattle, United States (2022). https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.341","DOI":"10.18653\/v1\/2022.naacl-main.341"},{"key":"13_CR40","doi-asserted-by":"publisher","unstructured":"Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38\u201345. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-demos.6","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"13_CR41","doi-asserted-by":"publisher","unstructured":"Xiang, W., Wang, B.: A survey of event extraction from text. IEEE Access 7, 173111\u2013173137 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2956831","DOI":"10.1109\/ACCESS.2019.2956831"},{"key":"13_CR42","doi-asserted-by":"publisher","unstructured":"Yu, W., et al.: A survey of knowledge-enhanced text generation. ACM Comput. Surv. 54(11s) (2022). https:\/\/doi.org\/10.1145\/3512467","DOI":"10.1145\/3512467"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42, 31\u201360 (2004). https:\/\/api.semanticscholar.org\/CorpusID:5387869","DOI":"10.1023\/A:1007652502315"},{"key":"13_CR44","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Feng, S., Tan, C.: Active example selection for in-context learning. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 9134\u20139148. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (2022). https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.622","DOI":"10.18653\/v1\/2022.emnlp-main.622"},{"key":"13_CR45","doi-asserted-by":"publisher","unstructured":"Zhao, L.: Event prediction in the big data era: a systematic survey. ACM Comput. Surv. 54(5) (2021). https:\/\/doi.org\/10.1145\/3450287","DOI":"10.1145\/3450287"},{"key":"13_CR46","doi-asserted-by":"publisher","unstructured":"Zhao, M., et al.: LMTurk: few-shot learners as crowdsourcing workers in a language-model-as-a-service framework. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 675\u2013692. Association for Computational Linguistics, Seattle, United States (2022). https:\/\/doi.org\/10.18653\/v1\/2022.findings-naacl.51","DOI":"10.18653\/v1\/2022.findings-naacl.51"},{"key":"13_CR47","doi-asserted-by":"crossref","unstructured":"Zheng, L., et al.: Judging LLM-as-a-judge with MT-bench and chatbot arena (2023)","DOI":"10.52202\/075280-2020"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77844-5_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T07:17:11Z","timestamp":1790061431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77844-5_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"ISBN":["9783031778438","9783031778445"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77844-5_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"27 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Hanover, MD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"11 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2024.semanticweb.org\/event\/3715c6fc-e2d7-47eb-8c01-5fe4ac589a52\/summary","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}