{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T08:38:18Z","timestamp":1766306298795,"version":"3.48.0"},"reference-count":32,"publisher":"Elsevier BV","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.elsevier.com\/tdm\/userlicense\/1.0\/"},{"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.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":261,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1016\/j.procs.2025.09.169","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T22:14:26Z","timestamp":1762467266000},"page":"505-514","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["SIS: Leveraging Semantically-Informed Similarity of Text Embeddings for Enhanced Ontology Alignment"],"prefix":"10.1016","volume":"270","author":[{"given":"Giulio","family":"Macilenti","sequence":"first","affiliation":[]},{"given":"Armando","family":"Stellato","sequence":"additional","affiliation":[]},{"given":"Manuel","family":"Fiorelli","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2025.09.169_bib1","unstructured":"Ehrig, M. Ontology alignment: bridging the semantic gap (Vol. 4). Springer Science & Business Media (2006)."},{"key":"10.1016\/j.procs.2025.09.169_bib2","doi-asserted-by":"crossref","unstructured":"Shvaiko, P., & Euzenat, J. Ontology matching: state of the art and future challenges. IEEE Transactions on knowledge and data engineering, 25(1), 158\u2013176 (2011).","DOI":"10.1109\/TKDE.2011.253"},{"key":"10.1016\/j.procs.2025.09.169_bib3","doi-asserted-by":"crossref","unstructured":"Hitzler, P., Janowicz, K., Haller, A., & Polleres, A. Are we better off with just one ontology on the Web?. Semantic Web, 11(1), 87\u201399 (2020).","DOI":"10.3233\/SW-190379"},{"key":"10.1016\/j.procs.2025.09.169_bib4","doi-asserted-by":"crossref","unstructured":"Janowicz, K., van Harmelen, F., Hendler, J. A., & Hitzler, P. Why the Data Train Needs Semantic Rails. AI Magazine, 36(1), 5\u201314 (2015).","DOI":"10.1609\/aimag.v36i1.2560"},{"key":"10.1016\/j.procs.2025.09.169_bib5","doi-asserted-by":"crossref","unstructured":"Faria, D., Pesquita, C., Mott, I., Martins, C., Couto, F. M., & Cruz, I. F. Tackling the challenges of matching biomedical ontologies. Journal of Biomedical Semantics, 9(9), 5\u201314 (2018).","DOI":"10.1186\/s13326-017-0170-9"},{"key":"10.1016\/j.procs.2025.09.169_bib6","doi-asserted-by":"crossref","unstructured":"Rahm, E., & Bernstein, P. A. A survey of approaches to automatic schema matching. The VLDB Journal, 10, 334\u2013350 (2001).","DOI":"10.1007\/s007780100057"},{"key":"10.1016\/j.procs.2025.09.169_bib7","unstructured":"Vaswani, A., et al. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"10.1016\/j.procs.2025.09.169_bib8","unstructured":"Zhang, S., et al. Instruction tuning for large language models: A survey. arXiv preprint arXiv:2308.10792 (2023)."},{"key":"10.1016\/j.procs.2025.09.169_bib9","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1162\/tacl_a_00638","article-title":"Lost in the middle: How language models use long contexts","volume":"12","author":"Liu","year":"2024","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"10.1016\/j.procs.2025.09.169_bib10","doi-asserted-by":"crossref","unstructured":"Macilenti, G., Stellato, A., Fiorelli, M. Prompting is not all you need: evaluating GPT-4 performance on a real world ontology alignment use case. Procedia Computer Science 246C (2024), 1289\u20131298.","DOI":"10.1016\/j.procs.2024.09.557"},{"key":"10.1016\/j.procs.2025.09.169_bib11","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C. D. GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014).","DOI":"10.3115\/v1\/D14-1162"},{"key":"10.1016\/j.procs.2025.09.169_bib12","doi-asserted-by":"crossref","unstructured":"Reimers, N., & Gurevych, I. Sentence-BERT: Sentence embeddings using siamese BERT networks. arXiv preprint arXiv:1908.10084 (2019).","DOI":"10.18653\/v1\/D19-1410"},{"key":"10.1016\/j.procs.2025.09.169_bib13","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, X., Lai, S., He, S., Liu, K., Zhao, J., & Lv, X. Ontology matching with word embeddings. In Chinese computational linguistics and natural language processing based on naturally annotated big data, Springer (2014).","DOI":"10.1007\/978-3-319-12277-9_4"},{"key":"10.1016\/j.procs.2025.09.169_bib14","doi-asserted-by":"crossref","unstructured":"Pedersen, T., Patwardhan, S., & Michelizzi, J. Wordnet similarity: Measuring the relatedness of concepts. Demonstration Papers at HLT-NAACL 2004. Association for Computational Linguistics (2004), 38\u201341.","DOI":"10.3115\/1614025.1614037"},{"key":"10.1016\/j.procs.2025.09.169_bib15","unstructured":"Mikolov, T. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)."},{"key":"10.1016\/j.procs.2025.09.169_bib16","unstructured":"Dhouib, M. T., Zucker, C. F., & Tettamanzi, A. G. An ontology alignment approach combining word embedding and the radius measure. In International Conference on Semantic Systems, Springer, Cham (2019)."},{"key":"10.1016\/j.procs.2025.09.169_bib17","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","article-title":"Enriching word vectors with subword information","volume":"5","author":"Bojanowski","year":"2017","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"10.1016\/j.procs.2025.09.169_bib18","unstructured":"Thieblin, E. Task-oriented complex alignments on conference organisation (2019). https:\/\/fgshare.com\/articles\/dataset\/Complexalignmentdatasetonconferenceorganisation\/4986368\/8."},{"key":"10.1016\/j.procs.2025.09.169_bib19","doi-asserted-by":"crossref","unstructured":"Kolyvakis, P., Kalousis, A., & Kiritsis, D. Deepalignment: Unsupervised ontology matching with refined word vectors. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics, 787\u2013798.","DOI":"10.18653\/v1\/N18-1072"},{"key":"10.1016\/j.procs.2025.09.169_bib20","doi-asserted-by":"crossref","unstructured":"Wang, L. L., Bhagavatula, C., Neumann, M., Lo, K., Wilhelm, C., & Ammar, W. Ontology alignment in the biomedical domain using entity definitions and context. arXiv preprint arXiv:1806.07976 (2018).","DOI":"10.18653\/v1\/W18-2306"},{"key":"10.1016\/j.procs.2025.09.169_bib21","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":"10.1016\/j.procs.2025.09.169_bib22","unstructured":"Neutel, S., & de Boer, M. H. T. Towards Automatic Ontology Alignment using BERT. AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering (2021)."},{"key":"10.1016\/j.procs.2025.09.169_bib23","doi-asserted-by":"crossref","unstructured":"He, Y., Chen, J., Antonyrajah, D., & Horrocks, I. BERTMap: a BERT-based ontology alignment system. Proceedings of the AAAI Conference on Artificial Intelligence 36(5): 5684\u20135691 (2022).","DOI":"10.1609\/aaai.v36i5.20510"},{"key":"10.1016\/j.procs.2025.09.169_bib24","unstructured":"Peng, Y., Alam, M., & Bonald, T. Ontology Matching using Textual Class Descriptions. Proceedings of the 18th International Workshop on Ontology Matching co-located with the 22nd International Semantic Web Conference (ISWC 2023), Athens, Greece, November 7, 2023."},{"key":"10.1016\/j.procs.2025.09.169_bib25","unstructured":"Norouzi, S. S., Mahdavinejad, M. S., & Hitzler, P. Conversational Ontology Alignment with ChatGPT. CoRR abs\/2308.09217 (2023)."},{"key":"10.1016\/j.procs.2025.09.169_bib26","doi-asserted-by":"crossref","unstructured":"Amini, R., Norouzi, S. S., Hitzler, P., & Amini, R. Towards Complex Ontology Alignment using Large Language Models. arXiv preprint arXiv:2404.10329 (2024).","DOI":"10.1007\/978-3-031-81221-7_2"},{"key":"10.1016\/j.procs.2025.09.169_bib27","unstructured":"He, Y., et al. Exploring large language models for ontology alignment. arXiv preprint arXiv:2309.07172 (2023)."},{"key":"10.1016\/j.procs.2025.09.169_bib28","unstructured":"Wang, Q., Gao, Z., & Xu, R. Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking. arXiv preprint arXiv:2307.01137 (2023)."},{"key":"10.1016\/j.procs.2025.09.169_bib29","doi-asserted-by":"crossref","unstructured":"He, Y., Chen, J., Dong, H., Jim\u00e9nez-Ruiz, E., Hadian, A., & Horrocks, I. Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. The Semantic Web\u2013ISWC 2022, Springer, Cham (2022), 575\u2013591.","DOI":"10.1007\/978-3-031-19433-7_33"},{"key":"10.1016\/j.procs.2025.09.169_bib30","doi-asserted-by":"crossref","unstructured":"Hertling, S., & Paulheim, H. OLaLa: Ontology matching with large language models. Proceedings of the 12th Knowledge Capture Conference 2023 (2023), 131\u2013139.","DOI":"10.1145\/3587259.3627571"},{"key":"10.1016\/j.procs.2025.09.169_bib31","unstructured":"Hertling, S., & Paulheim, H. OLaLa results for OAEI 2023. OM@ISWC (2023)."},{"key":"10.1016\/j.procs.2025.09.169_bib32","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.websem.2017.01.001","article-title":"The ten-year ontofarm and its fertilization within the onto-sphere","volume":"43","author":"Zamazal","year":"2017","journal-title":"Journal of Web Semantics"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S187705092502839X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S187705092502839X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T08:33:49Z","timestamp":1766306029000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S187705092502839X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":32,"alternative-id":["S187705092502839X"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2025.09.169","relation":{},"ISSN":["1877-0509"],"issn-type":[{"type":"print","value":"1877-0509"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SIS: Leveraging Semantically-Informed Similarity of Text Embeddings for Enhanced Ontology Alignment","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2025.09.169","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}