{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:09:43Z","timestamp":1767319783111,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819538294","type":"print"},{"value":"9789819538300","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\/978-981-95-3830-0_21","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:05:52Z","timestamp":1767319552000},"page":"321-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Memorization to\u00a0Discovery: A Novel Benchmark for\u00a0Relational Triple Extraction"],"prefix":"10.1007","author":[{"given":"Aoran","family":"Gan","sequence":"first","affiliation":[]},{"given":"Ye","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hongbo","family":"Gang","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guoping","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"21_CR1","unstructured":"Axelsson, A., Skantze, G.: Using large language models for zero-shot natural language generation from knowledge graphs. In: Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023), pp. 39\u201354. Association for Computational Linguistics (2023)"},{"key":"21_CR2","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR3","unstructured":"Chan, Y.S., Roth, D.: Exploiting syntactico-semantic structures for relation extraction. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 551\u2013560. Association for Computational Linguistics (2011)"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, L., et al.: ENT-DESC: entity description generation by exploring knowledge graph. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1187\u20131197. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.90"},{"key":"21_CR5","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 (2018)"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 601\u2013610 (2014)","DOI":"10.1145\/2623330.2623623"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Fu, T.J., Li, P.H., Ma, W.Y.: GraphRel: modeling text as relational graphs for joint entity and relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1409\u20131418. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/P19-1136"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Gardent, C., Shimorina, A., Narayan, S., Perez-Beltrachini, L.: Creating training corpora for NLG micro-planners. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 179\u2013188. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/P17-1017"},{"key":"21_CR9","unstructured":"Guan, X., et al.: Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting. CoRR abs\/2311.13314 (2023). https:\/\/doi.org\/10.48550\/arXiv.2311.13314"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Hennig, L., Thomas, P., M\u00f6ller, S.: MultiTACRED: a multilingual version of the TAC relation extraction dataset. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3785\u20133801. Association for Computational Linguistics (2023)","DOI":"10.18653\/v1\/2023.acl-long.210"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Josifoski, M., Sakota, M., Peyrard, M., West, R.: Exploiting asymmetry for synthetic training data generation: SynthIE and the case of information extraction. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 1555\u20131574. Association for Computational Linguistics (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.96"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Lee, J., Lee, M.J., Yang, J.Y., Yang, E.: Does it really generalize well on unseen data? Systematic evaluation of relational triple extraction methods. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3849\u20133858. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.naacl-main.282"},{"key":"21_CR13","doi-asserted-by":"publisher","unstructured":"Liang, J., He, Q., Zhang, D., Fan, S.: Extraction of joint entity and relationships with soft pruning and globalpointer. Appl. Sci. 12(13) (2022). https:\/\/doi.org\/10.3390\/app12136361. https:\/\/www.mdpi.com\/2076-3417\/12\/13\/6361","DOI":"10.3390\/app12136361"},{"issue":"1","key":"21_CR14","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/TKDE.2019.2924374","volume":"33","author":"Q Liu","year":"2019","unstructured":"Liu, Q., et al.: EKT: exercise-aware knowledge tracing for student performance prediction. IEEE Trans. Knowl. Data Eng. 33(1), 100\u2013115 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Empowering few-shot relation extraction with the integration of traditional re methods and large language models. In: International Conference on Database Systems for Advanced Applications, pp. 349\u2013359. Springer (2024)","DOI":"10.1007\/978-981-97-5569-1_22"},{"key":"21_CR16","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1007\/s12559-021-09917-7","volume":"13","author":"T Nayak","year":"2021","unstructured":"Nayak, T., Majumder, N., Goyal, P., Poria, S.: Deep neural approaches to relation triplets extraction: a comprehensive survey. Cogn. Comput. 13, 1215\u20131232 (2021)","journal-title":"Cogn. Comput."},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Ning, J., Yang, Z., Sun, Y., Wang, Z., Lin, H.: OD-RTE: a one-stage object detection framework for relational triple extraction. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 11120\u201311135. Association for Computational Linguistics (2023)","DOI":"10.18653\/v1\/2023.acl-long.623"},{"key":"21_CR18","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730\u201327744 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Papaluca, A., Krefl, D., Rodr\u00edguez\u00a0M\u00e9ndez, S., Lensky, A., Suominen, H.: Zero- and few-shots knowledge graph triplet extraction with large language models. In: Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024), pp. 12\u201323. Association for Computational Linguistics (2024)","DOI":"10.18653\/v1\/2024.kallm-1.2"},{"issue":"8","key":"21_CR20","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Ren, F., et al.: A novel global feature-oriented relational triple extraction model based on table filling. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2646\u20132656. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.208"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Ren, F., Zhang, L., Zhao, X., Yin, S., Liu, S., Li, B.: A simple but effective bidirectional framework for relational triple extraction. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (2021)","DOI":"10.1145\/3488560.3498409"},{"key":"21_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/978-3-642-15939-8_10","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"S Riedel","year":"2010","unstructured":"Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Balc\u00e1zar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6323, pp. 148\u2013163. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15939-8_10"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Shang, Y.M., Huang, H., Mao, X.: Onerel: joint entity and relation extraction with one module in one step. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 11285\u201311293 (2022)","DOI":"10.1609\/aaai.v36i10.21379"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Shang, Y.M., Huang, H., Sun, X., Wei, W., Mao, X.L.: Relational triple extraction: one step is enough. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 4360\u20134366. International Joint Conferences on Artificial Intelligence Organization (2022)","DOI":"10.24963\/ijcai.2022\/605"},{"key":"21_CR26","doi-asserted-by":"publisher","unstructured":"Sui, D., Zeng, X., Chen, Y., Liu, K., Zhao, J.: Joint entity and relation extraction with set prediction networks. IEEE Trans. Neural Netw. Learn. Syst. 1\u201312 (2023). https:\/\/doi.org\/10.1109\/TNNLS.2023.3264735","DOI":"10.1109\/TNNLS.2023.3264735"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Tang, W., et al.: UniRel: unified representation and interaction for joint relational triple extraction. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 7087\u20137099. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.477"},{"issue":"8","key":"21_CR28","doi-asserted-by":"publisher","first-page":"8312","DOI":"10.1109\/TKDE.2022.3201037","volume":"35","author":"F Wang","year":"2022","unstructured":"Wang, F., et al.: Neuralcd: a general framework for cognitive diagnosis. IEEE Trans. Knowl. Data Eng. 35(8), 8312\u20138327 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"21_CR29","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. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1572\u20131582. International Committee on Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.coling-main.138"},{"key":"21_CR30","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. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1476\u20131488. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Vlachos, A.: Zero-shot fact-checking with semantic triples and knowledge graphs. arXiv preprint arXiv:2312.11785 (2023)","DOI":"10.18653\/v1\/2024.kallm-1.11"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pp. 71\u201378. Association for Computational Linguistics (2002)","DOI":"10.3115\/1118693.1118703"},{"issue":"1","key":"21_CR33","first-page":"377","volume":"35","author":"K Zhang","year":"2021","unstructured":"Zhang, K., et al.: EATN: an efficient adaptive transfer network for aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 35(1), 377\u2013389 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, H., Liu, Q., Zhao, H., Zhu, H., Chen, E.: Interactive attention transfer network for cross-domain sentiment classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 5773\u20135780 (2019)","DOI":"10.1609\/aaai.v33i01.33015773"},{"key":"21_CR35","doi-asserted-by":"publisher","unstructured":"Zhao, K., Xu, H., Cheng, Y., Li, X., Gao, K.: Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction. Knowl.-Based Syst. 106888 (2021). https:\/\/doi.org\/10.1016\/j.knosys.2021.106888. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0950705121001519","DOI":"10.1016\/j.knosys.2021.106888"},{"key":"21_CR36","doi-asserted-by":"crossref","unstructured":"Zheng, H., et al.: PRGC: potential relation and global correspondence based joint relational triple extraction. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 6225\u20136235. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.486"},{"key":"21_CR37","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. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1227\u20131236. Association for Computational Linguistics (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\/978-981-95-3830-0_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:05:56Z","timestamp":1767319556000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3830-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819538294","9789819538300"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3830-0_21","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"}}]}}