{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T04:48:12Z","timestamp":1751690892594,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031443541"},{"type":"electronic","value":"9783031443558"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44355-8_2","type":"book-chapter","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T06:01:44Z","timestamp":1698213704000},"page":"16-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["New Siamese Neural Networks for\u00a0Text Classification and\u00a0Ontologies Alignment"],"prefix":"10.1007","author":[{"given":"Safaa","family":"Menad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wissame","family":"Laddada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sa\u00efd","family":"Abdedda\u00efm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina F.","family":"Soualmia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,26]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Alsentzer, E., et al.: Publicly available clinical BERT embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 72\u201378. Association for Computational Linguistics, Minneapolis, Minnesota, USA (2019)","DOI":"10.18653\/v1\/W19-1909"},{"issue":"4","key":"2_CR2","doi-asserted-by":"publisher","first-page":"339","DOI":"10.29252\/beat-070401","volume":"7","author":"MB Ayalew","year":"2019","unstructured":"Ayalew, M.B., Tegegn, H.G., Abdela, O.A.: Drug related hospital admissions; a systematic review of the recent literatures. Bull. Emerg. Trauma 7(4), 339 (2019)","journal-title":"Bull. Emerg. Trauma"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3615\u20133620 (2019)","DOI":"10.18653\/v1\/D19-1371"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Chicco, D.: Siamese neural networks: an overview. In: Artificial Neural Networks, pp. 73\u201394 (2021)","DOI":"10.1007\/978-1-0716-0826-5_3"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Chua, W.W.K., Jae Kim, J.: BOAT: automatic alignment of biomedical ontologies using term informativeness and candidate selection. J. Biomed. Inform. 45(2), 337\u2013349 (2012)","DOI":"10.1016\/j.jbi.2011.11.010"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Cohan, A., Feldman, S., Beltagy, I., Downey, D., Weld, D.S.: Specter: Document-level representation learning using citation-informed transformers. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2270\u20132282 (2020)","DOI":"10.18653\/v1\/2020.acl-main.207"},{"key":"2_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171\u20134186 (2019)"},{"key":"2_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38721-0","volume-title":"Ontology Matching","author":"J Euzenat","year":"2013","unstructured":"Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38721-0"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., Chen, D.: Simcse: simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6894\u20136910 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"issue":"1","key":"2_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3458754","volume":"3","author":"Y Gu","year":"2022","unstructured":"Gu, Y., et al.: Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthc. 3(1), 1\u201323 (2022)","journal-title":"ACM Trans. Comput. Healthc."},{"issue":"1","key":"2_CR11","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/S0092-8674(00)81683-9","volume":"100","author":"D Hanahan","year":"2000","unstructured":"Hanahan, D., Weinberg, R.A.: The hallmarks of cancer. Cell 100(1), 57\u201370 (2000)","journal-title":"Cell"},{"key":"2_CR12","unstructured":"Hertling, S., Portisch, J., Paulheim, H.: Matching with transformers in melt (2021)"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Jin, Q., Dhingra, B., Liu, Z., Cohen, W., Lu, X.: PubMedQA: a dataset for biomedical research question answering. In: Proceedings of (EMNLP-IJCNLP), pp. 2567\u20132577 (2019)","DOI":"10.18653\/v1\/D19-1259"},{"issue":"1","key":"2_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3(1), 1\u20139 (2016)","journal-title":"Sci. Data"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Kanakarajan, K.r., Kundumani, B., Sankarasubbu, M.: BioELECTRA: pretrained biomedical text encoder using discriminators. In: Proceedings of the 20th Workshop on Biomedical Language Processing, pp. 143\u2013154. Association for Computational Linguistics, Online (2021)","DOI":"10.18653\/v1\/2021.bionlp-1.16"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Kolyvakis, P., Kalousis, A., Kiritsis, D.: DeepAlignment: unsupervised ontology matching with refined word vectors. In: Proceedings of NAACL-HLT, 787\u2013798, pp. 787\u2013798 (2018)","DOI":"10.18653\/v1\/N18-1072"},{"issue":"4","key":"2_CR17","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Liu, F., Shareghi, E., Meng, Z., Basaldella, M., Collier, N.: Self-alignment pretraining for biomedical entity representations. In: Proceedings of NAACL-HLT, pp. 4228\u20134238 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.334"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Mary, M., Soualmia, L., Gansel, X., Darmoni, S., Karlsson, D., Schulz, S.: Ontological representation of laboratory test observables: challenges and perspectives in the snomed CT observable entity model adoption, pp. 14\u201323 (2017)","DOI":"10.1007\/978-3-319-59758-4_2"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Muennighoff, N., Tazi, N., Magne, L., Reimers, N.: MTEB: massive text embedding benchmark. arXiv preprint arXiv:2210.07316 (2022)","DOI":"10.18653\/v1\/2023.eacl-main.148"},{"key":"2_CR21","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1007\/978-3-030-43887-6_51","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"A Nentidis","year":"2020","unstructured":"Nentidis, A., Bougiatiotis, K., Krithara, A., Paliouras, G.: Results of the seventh edition of the BioASQ challenge. In: Cellier, P., Driessens, K. (eds.) ECML PKDD 2019. CCIS, vol. 1168, pp. 553\u2013568. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43887-6_51"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Ormerod, M., Mart\u00ednez del Rinc\u00f3n, J., Devereux, B.: Predicting semantic similarity between clinical sentence pairs using transformer models: evaluation and representational analysis. JMIR Med. Inform. 9(5), e23099 (2021)","DOI":"10.2196\/23099"},{"key":"2_CR23","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.inffus.2021.01.007","volume":"71","author":"I Osman","year":"2021","unstructured":"Osman, I., Ben Yahia, S., Diallo, G.: Ontology integration: approaches and challenging issues. Inf. Fusion 71, 38\u201363 (2021)","journal-title":"Inf. Fusion"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Peng, Y., Yan, S., Lu, Z.: Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets. In: Proceedings of the 18th BioNLP Workshop and Shared Task, pp. 58\u201365 (2019)","DOI":"10.18653\/v1\/W19-5006"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Peng, Y., Yan, S., Lu, Z.: Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets. In: Proceedings of the 18th BioNLP Workshop and Shared Task, pp. 58\u201365. Association for Computational Linguistics, Florence, Italy (2019)","DOI":"10.18653\/v1\/W19-5006"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Portisch, J., Hladik, M., Paulheim, H.: Background knowledge in ontology matching: a survey. Semantic Web, pp. 1\u201355 (2022)","DOI":"10.3233\/SW-223085"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Proceedings of (EMNLP-IJCNLP), pp. 3982\u20133992. Association for Computational Linguistics, Hong Kong, China (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"2_CR28","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1109\/TKDE.2011.253","volume":"25","author":"P Shvaiko","year":"2013","unstructured":"Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25, 158\u2013176 (2013)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2_CR29","unstructured":"Vela, J., Gracia, J.: Cross-lingual ontology matching with CIDER-LM: results for OAEI 2022 (2022)"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Wang, K., Reimers, N., Gurevych, I.: TSDAE: using transformer-based sequential denoising auto-encoderfor unsupervised sentence embedding learning. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 671\u2013688 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.59"},{"key":"2_CR31","first-page":"5776","volume":"33","author":"W Wang","year":"2020","unstructured":"Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., Zhou, M.: MiniLM: deep self-attention distillation for task-agnostic compression of pre-trained transformers. Adv. Neural. Inf. Process. Syst. 33, 5776\u20135788 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Wu, J., Lv, J., Guo, H., Ma, S.: DAEOM: a deep attentional embedding approach for biomedical ontology matching. Appl. Sci. 10(21) (2020)","DOI":"10.3390\/app10217909"},{"key":"2_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/11926078_2","volume-title":"The Semantic Web - ISWC 2006","author":"A Zimmermann","year":"2006","unstructured":"Zimmermann, A., Euzenat, J.: Three semantics for distributed systems and their relations with alignment composition. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 16\u201329. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11926078_2"}],"container-title":["Lecture Notes in Computer Science","Complex Computational Ecosystems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44355-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T06:02:12Z","timestamp":1698213732000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44355-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031443541","9783031443558"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44355-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Complex Computational Ecosystems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Baku","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Azerbaijan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cce2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cce-2023.ufaz.az\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Number and type of other papers accepted: 4 Keynote abstracts","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}