{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:40:14Z","timestamp":1770748814645,"version":"3.50.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031945748","type":"print"},{"value":"9783031945755","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-94575-5_24","type":"book-chapter","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T02:15:17Z","timestamp":1748657717000},"page":"441-459","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["RelCheck: Improving Relation Extraction with\u00a0Ontology-Guided and\u00a0LLM-Based Validation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7677-9845","authenticated-orcid":false,"given":"Mounir","family":"Ourekouch","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3284-1237","authenticated-orcid":false,"given":"Mohammed-Amine","family":"Koulali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Erradi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,1]]},"reference":[{"key":"24_CR1","unstructured":"Achiam, J., et\u00a0al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"24_CR2","first-page":"1877","volume":"33","author":"TB Brown","year":"2020","unstructured":"Brown, T.B., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"240","key":"24_CR3","first-page":"1","volume":"24","author":"A Chowdhery","year":"2023","unstructured":"Chowdhery, A., et al.: Palm: scaling language modeling with pathways. J. Mach. Learn. Res. 24(240), 1\u2013113 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR4","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: 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, vol. 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":"24_CR5","doi-asserted-by":"publisher","unstructured":"Guo, Z., Zhang, Y., Lu, W.: Attention guided graph convolutional networks for relation extraction. In: Korhonen, A., Traum, D., M\u00e0rquez, L. (eds.) Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 241\u2013251. Association for Computational Linguistics, Florence, Italy (2019). https:\/\/doi.org\/10.18653\/v1\/P19-1024","DOI":"10.18653\/v1\/P19-1024"},{"key":"24_CR6","unstructured":"Hogan, W., Shang, J.: Entangled relations: leveraging NLI and meta-analysis to enhance biomedical relation extraction. arXiv preprint arXiv:2406.00226 (2024)"},{"key":"24_CR7","doi-asserted-by":"publisher","unstructured":"Jimenez\u00a0Gutierrez, B., et al.: Thinking about GPT-3 in-context learning for biomedical IE? think again. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 4497\u20134512. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (2022). https:\/\/doi.org\/10.18653\/v1\/2022.findings-emnlp.329","DOI":"10.18653\/v1\/2022.findings-emnlp.329"},{"key":"24_CR8","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1162\/tacl_a_00300","volume":"8","author":"M Joshi","year":"2020","unstructured":"Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: SpanBERT: improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguist. 8, 64\u201377 (2020)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"24_CR9","doi-asserted-by":"publisher","unstructured":"Lu, K., Hsu, I.H., Zhou, W., Ma, M.D., Chen, M.: Summarization as indirect supervision for relation extraction. In: Goldberg, Y., Kozareva, Z., Zhang, Y. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2022. pp. 6575\u20136594. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (2022). https:\/\/doi.org\/10.18653\/v1\/2022.findings-emnlp.490","DOI":"10.18653\/v1\/2022.findings-emnlp.490"},{"key":"24_CR10","doi-asserted-by":"publisher","unstructured":"Miwa, M., Bansal, M.: End-to-end relation extraction using LSTMs on sequences and tree structures. In: Erk, K., Smith, N.A. (eds.) Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1105\u20131116. Association for Computational Linguistics, Berlin, Germany (2016). https:\/\/doi.org\/10.18653\/v1\/P16-1105","DOI":"10.18653\/v1\/P16-1105"},{"key":"24_CR11","doi-asserted-by":"publisher","unstructured":"Sainz, O., Lopez\u00a0de Lacalle, O., Labaka, G., Barrena, A., Agirre, E.: Label verbalization and entailment for effective zero and few-shot relation extraction. In: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 1199\u20131212. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.92","DOI":"10.18653\/v1\/2021.emnlp-main.92"},{"key":"24_CR12","unstructured":"Soares, L.B., FitzGerald, N., Ling, J., Kwiatkowski, T.: Matching the blanks: distributional similarity for relation learning. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2895\u20132905 (2019)"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Stoica, G., Platanios, E.A., P\u00f3czos, B.: Re-TACRED: addressing shortcomings of the tacred dataset. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 13843\u201313850 (2021)","DOI":"10.1609\/aaai.v35i15.17631"},{"key":"24_CR14","unstructured":"Touvron, H., et al.: Llama: open and efficient foundation language models. ArXiv abs\/2302.13971 (2023). https:\/\/api.semanticscholar.org\/CorpusID:257219404"},{"issue":"2","key":"24_CR15","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1017\/S0269888900007797","volume":"11","author":"M Uschold","year":"1996","unstructured":"Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93\u2013136 (1996)","journal-title":"Knowl. Eng. Rev."},{"key":"24_CR16","doi-asserted-by":"publisher","unstructured":"Wadhwa, S., Amir, S., Wallace, B.: Revisiting relation extraction in the era of large language models. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, vol. 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":"24_CR17","doi-asserted-by":"crossref","unstructured":"Wang, H., Qin, K., Zakari, R.Y., Lu, G., Yin, J.: Deep neural network-based relation extraction: an overview. Neural Comput. Appl. 1\u201321 (2022)","DOI":"10.1007\/s00521-021-06667-3"},{"key":"24_CR18","unstructured":"Wu, S., He, Y., Wang, K., Liu, K., Zhao, J.: Enriching pre-trained language model with entity information for relation classification. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2363\u20132368 (2019)"},{"key":"24_CR19","unstructured":"Yang, Z.: Xlnet: generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237 (2019)"},{"key":"24_CR20","doi-asserted-by":"publisher","unstructured":"Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: M\u00e0rquez, L., Callison-Burch, C., Su, J. (eds.) Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1753\u20131762. Association for Computational Linguistics, Lisbon, Portugal (2015). https:\/\/doi.org\/10.18653\/v1\/D15-1203","DOI":"10.18653\/v1\/D15-1203"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, K., Guti\u00e9rrez, B.J., Su, Y.: Aligning instruction tasks unlocks large language models as zero-shot relation extractors. arXiv preprint arXiv:2305.11159 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.50"},{"key":"24_CR22","unstructured":"Zhang, S., et al.: OPT: open pre-trained transformer language models (2022)"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. arXiv preprint arXiv:1809.10185 (2018)","DOI":"10.18653\/v1\/D18-1244"},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhong, V., Chen, D., Angeli, G., Manning, C.D.: Position-aware attention and supervised data improve slot filling. In: Conference on Empirical Methods in Natural Language Processing (2017)","DOI":"10.18653\/v1\/D17-1004"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, W., Chen, M.: An improved baseline for sentence-level relation extraction. In: He, Y., Ji, H., Li, S., Liu, Y., Chang, C.H. (eds.) Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processingm, vol. 2: Short Papers, pp. 161\u2013168. Association for Computational Linguistics (2022). https:\/\/aclanthology.org\/2022.aacl-short.21","DOI":"10.18653\/v1\/2022.aacl-short.21"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-94575-5_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T02:15:38Z","timestamp":1748657738000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-94575-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031945748","9783031945755"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-94575-5_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portoroz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovenia","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":"1 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esws2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2025.eswc-conferences.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}