{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:40:32Z","timestamp":1767321632204,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819541577","type":"print"},{"value":"9789819541584","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-4158-4_33","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:38:37Z","timestamp":1767321517000},"page":"459-470","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Relation Discovery via\u00a0Graph Neural Networks in\u00a0the\u00a0Era of\u00a0Large Language Model"],"prefix":"10.1007","author":[{"given":"Zhiqiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Alrowili, S., Vijay-Shanker, K.: Biom-transformers: building large biomedical language models with bert, albert and electra. In: Proceedings of the 20th Workshop on Biomedical Language Processing, pp. 221\u2013227 (2021)","DOI":"10.18653\/v1\/2021.bionlp-1.24"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-015-0472-9","volume":"16","author":"\u00c0 Bravo","year":"2015","unstructured":"Bravo, \u00c0., Pi\u00f1ero, J., Queralt-Rosinach, N., Rautschka, M., Furlong, L.I.: Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinf. 16, 1\u201317 (2015)","journal-title":"BMC Bioinf."},{"key":"33_CR3","doi-asserted-by":"publisher","unstructured":"Chen, G., Tian, Y., Song, Y., Wan, X.: Relation extraction with type-aware map memories of word dependencies. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Findings of the Association for Computational Linguistics: ACL\/IJCNLP 2021, Online Event, 1\u20136 August 2021. Findings of ACL, vol. ACL\/IJCNLP 2021, pp. 2501\u20132512. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.221, https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.221","DOI":"10.18653\/v1\/2021.findings-acl.221"},{"key":"33_CR4","doi-asserted-by":"publisher","unstructured":"Denecke, K.: Sentiment Analysis in the Medical Domain. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-30187-2","DOI":"10.1007\/978-3-031-30187-2"},{"key":"33_CR5","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, vol. 1 (Long and Short Papers). pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Guo, Z., Zhang, Y., Lu, W.: Attention guided graph convolutional networks for relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 241\u2013251 (2019)","DOI":"10.18653\/v1\/P19-1024"},{"issue":"5","key":"33_CR7","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.jbi.2013.07.011","volume":"46","author":"M Herrero-Zazo","year":"2013","unstructured":"Herrero-Zazo, M., Segura-Bedmar, I., Mart\u00ednez, P., Declerck, T.: The DDI corpus: an annotated corpus with pharmacological substances and drug-drug interactions. J. Biomed. Inf. 46(5), 914\u2013920 (2013)","journal-title":"J. Biomed. Inf."},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for information extraction. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, 21\u201326 July 2004 - Poster and Demonstration. ACL (2004). https:\/\/aclanthology.org\/P04-3022\/","DOI":"10.3115\/1219044.1219066"},{"key":"33_CR9","unstructured":"Krallinger, M., et\u00a0al.: Overview of the biocreative vi chemical-protein interaction track. In: Proceedings of the sixth BioCreative Challenge Evaluation Workshop, vol.\u00a01, pp. 141\u2013146 (2017)"},{"issue":"4","key":"33_CR10","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J.: Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"33_CR11","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"33_CR12","doi-asserted-by":"publisher","unstructured":"P\u00e9rez-P\u00e9rez, M., Ferreira, T., Igrejas, G., Fdez-Riverola, F.: A deep learning relation extraction approach to support a biomedical semi-automatic curation task: the case of the gluten bibliome. Expert Syst. Appl. 195, 116616 (2022) https:\/\/doi.org\/10.1016\/j.eswa.2022.116616","DOI":"10.1016\/j.eswa.2022.116616"},{"key":"33_CR13","unstructured":"Phan, L.N., et al.: Scifive: a text-to-text transformer model for biomedical literature. arXiv preprint arXiv:2106.03598 (2021)"},{"key":"33_CR14","first-page":"20346","volume":"34","author":"S Sheng","year":"2021","unstructured":"Sheng, S., et al.: Human-adversarial visual question answering. Adv. Neural. Inf. Process. Syst. 34, 20346\u201320359 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Tan, H., et al.: Joint biomedical entity and relation extraction with unified interaction maps. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1437\u20131442. IEEE (2023)","DOI":"10.1109\/BIBM58861.2023.10385642"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Tian, Y., Chen, G., Song, Y., Wan, X.: Dependency-driven relation extraction with attentive graph convolutional networks. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 1: Long Papers, pp. 4458\u20134471 (2021)","DOI":"10.18653\/v1\/2021.acl-long.344"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Tinn, R., et al.: Fine-tuning large neural language models for biomedical natural language processing. Patterns 4(4) (2023)","DOI":"10.1016\/j.patter.2023.100729"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the Web Conference 2021, pp. 878\u2013887 (2021)","DOI":"10.1145\/3442381.3450133"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yu, B., Zhang, Y., Liu, T., Zhu, H., Sun, L.: Tplinker: single-stage joint extraction of entities and relations through token pair linking. arXiv preprint arXiv:2010.13415 (2020)","DOI":"10.18653\/v1\/2020.coling-main.138"},{"key":"33_CR20","doi-asserted-by":"crossref","unstructured":"Wei, Z., Su, J., Wang, Y., Tian, Y., Chang, Y.: A novel cascade binary tagging framework for relational triple extraction. arXiv preprint arXiv:1909.03227 (2019)","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Xu, J., Ma, M.D., Chen, M.: Can nli provide proper indirect supervision for low-resource biomedical relation extraction? arXiv preprint arXiv:2212.10784 (2022)","DOI":"10.18653\/v1\/2023.acl-long.138"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Xu, Y., Mou, L., Li, G., Chen, Y., Peng, H., Jin, Z.: Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1785\u20131794 (2015)","DOI":"10.18653\/v1\/D15-1206"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Yan, Q., Zhang, Y., Liu, Q., Wu, S., Wang, L.: Relation-aware heterogeneous graph for user profiling. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3573\u20133577 (2021)","DOI":"10.1145\/3459637.3482170"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Yasunaga, M., Leskovec, J., Liang, P.: Linkbert: pretraining language models with document links. arXiv preprint arXiv:2203.15827 (2022)","DOI":"10.18653\/v1\/2022.acl-long.551"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Yu, B., Mengge, X., Zhang, Z., Liu, T., Yubin, W., Wang, B.: Learning to prune dependency trees with rethinking for neural relation extraction. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 3842\u20133852 (2020)","DOI":"10.18653\/v1\/2020.coling-main.341"},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Liu, Y., Tan, C., Huang, S., Huang, F.: Improving biomedical pretrained language models with knowledge. arXiv preprint arXiv:2104.10344 (2021)","DOI":"10.18653\/v1\/2021.bionlp-1.20"},{"key":"33_CR27","unstructured":"Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083\u20131106 (2003). http:\/\/jmlr.org\/papers\/v3\/zelenko03a.html"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2205\u20132215 (2018)","DOI":"10.18653\/v1\/D18-1244"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., Xu, B.: Joint extraction of entities and relations based on a novel tagging scheme. arXiv preprint arXiv:1706.05075 (2017)","DOI":"10.18653\/v1\/P17-1113"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4158-4_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:38:40Z","timestamp":1767321520000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4158-4_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819541577","9789819541584"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4158-4_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"26 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2025.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}