{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T07:38:11Z","timestamp":1782632291490,"version":"3.54.5"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001664","name":"Leibniz-Gemeinschaft","doi-asserted-by":"publisher","award":["P99\/2020"],"award-info":[{"award-number":["P99\/2020"]}],"id":[{"id":"10.13039\/501100001664","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018693","name":"HORIZON EUROPE Framework Programme","doi-asserted-by":"publisher","award":["875160"],"award-info":[{"award-number":["875160"]}],"id":[{"id":"10.13039\/100018693","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:sec>\n            <jats:title>Abstract<\/jats:title>\n            <jats:p>Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, clinical decision-making, and treatment optimization. We present the Neuro-Symbolic System for Cancer (NSSC), a hybrid AI framework that integrates neurosymbolic methods with named entity recognition (NER) and entity linking (EL) to transform unstructured clinical notes into structured terms using medical vocabularies, with the Unified Medical Language System (UMLS) as a case study. NSSC was evaluated on a dataset of clinical notes from breast cancer patients, demonstrating significant improvements in the accuracy of both entity recognition and linking compared to state-of-the-art models. Specifically, NSSC achieved a 33% improvement over BioFalcon and a 58% improvement over scispaCy. By combining large language models (LLMs) with symbolic reasoning, NSSC improves the recognition and interoperability of oncologic entities, enabling seamless integration with existing biomedical knowledge. This approach marks a significant advancement in extracting meaningful information from clinical narratives, offering promising applications in cancer research and personalized patient care.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1007\/s11517-024-03227-4","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T06:03:23Z","timestamp":1730441003000},"page":"749-772","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes"],"prefix":"10.1007","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6377-8150","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Garc\u00eda-Barrag\u00e1n","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8007-7021","authenticated-orcid":false,"given":"Ahmad","family":"Sakor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1160-8727","authenticated-orcid":false,"given":"Maria-Esther","family":"Vidal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5615-6798","authenticated-orcid":false,"given":"Ernestina","family":"Menasalvas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Cristobal Sanchez","family":"Gonzalez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariano","family":"Provencio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3937-2269","authenticated-orcid":false,"given":"V\u00edctor","family":"Robles","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"3227_CR1","unstructured":"https:\/\/www.cancer.org\/cancer\/types\/breast-cancer\/about.html"},{"issue":"21","key":"3227_CR2","doi-asserted-by":"publisher","first-page":"11149","DOI":"10.1073\/pnas.200327197","volume":"97","author":"LAN Amaral","year":"2000","unstructured":"Amaral LAN, Scala A, Barthelemy M, Stanley HE (2000) Classes of small-world networks. Proc Natl Acad Sci 97(21):11149\u201311152","journal-title":"Proc Natl Acad Sci"},{"issue":"9","key":"3227_CR3","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.ijmedinf.2014.06.009","volume":"83","author":"I Spasi\u0107","year":"2014","unstructured":"Spasi\u0107 I, Livsey J, Keane JA, Nenadi\u0107 G (2014) Text mining of cancer-related information: review of current status and future directions. Int J Med Inform 83(9):605\u2013623. https:\/\/doi.org\/10.1016\/j.ijmedinf.2014.06.009","journal-title":"Int J Med Inform"},{"key":"3227_CR4","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1200\/CCI.20.00020","volume":"4","author":"KL Kehl","year":"2020","unstructured":"Kehl KL, Xu W, Lepisto E, Elmarakeby H, Hassett MJ, Van Allen EM, Johnson BE, Schrag D (2020) Natural language processing to ascertain cancer outcomes from medical oncologist notes. JCO Clin Cancer Inform 4:680\u2013690","journal-title":"JCO Clin Cancer Inform"},{"key":"3227_CR5","doi-asserted-by":"publisher","unstructured":"Bose P, Srinivasan S, Sleeman WC, Palta J, Kapoor R, Ghosh P (2021) A survey on recent named entity recognition and relationship extraction techniques on clinical texts. Appl Sci (Switzerland) 11(18). https:\/\/doi.org\/10.3390\/app11188319","DOI":"10.3390\/app11188319"},{"key":"3227_CR6","doi-asserted-by":"publisher","unstructured":"Zeng Z, Deng Y, Li X, Naumann T, Luo Y (2019) Natural language processing for EHR-based computational phenotyping. IEEE\/ACM Trans Comput Biol Bioinform 16(1):139\u2013153. https:\/\/doi.org\/10.1109\/TCBB.2018.2849968arXiv:1806.04820","DOI":"10.1109\/TCBB.2018.2849968"},{"key":"3227_CR7","unstructured":"Zhou Y, Ju C, Caufield JH, Shih K, Chen C, Sun Y, Chang K-W, Ping P, Wang W (2021) Clinical named entity recognition using contextualized token representations. arXiv:2106.12608"},{"issue":"4","key":"3227_CR8","doi-asserted-by":"publisher","first-page":"33799","DOI":"10.2196\/33799","volume":"10","author":"X Yang","year":"2022","unstructured":"Yang X, Mu D, Peng H, Li H, Wang Y, Wang P, Wang Y, Han S et al (2022) Research and application of artificial intelligence based on electronic health records of patients with cancer: systematic review. JMIR Med Inform 10(4):33799","journal-title":"JMIR Med Inform"},{"issue":"September","key":"3227_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.103985","volume":"132","author":"X Zhang","year":"2019","unstructured":"Zhang X, Zhang Y, Zhang Q, Ren Y, Qiu T, Ma J, Sun Q (2019) Extracting comprehensive clinical information for breast cancer using deep learning methods. Int J Med Inform 132(September):103985. https:\/\/doi.org\/10.1016\/j.ijmedinf.2019.103985","journal-title":"Int J Med Inform"},{"key":"3227_CR10","first-page":"876","volume":"2017","author":"T Hernandez-Boussard","year":"2017","unstructured":"Hernandez-Boussard T, Kourdis PD, Seto T, Ferrari M, Blayney DW, Rubin D, Brooks JD (2017) Mining electronic health records to extract patient-centered outcomes following prostate cancer treatment. AMIA. Annual Symposium proceedings. AMIA Symposium 2017:876\u2013882","journal-title":"Annual Symposium proceedings. AMIA Symposium"},{"key":"3227_CR11","doi-asserted-by":"crossref","unstructured":"Solarte-Pab\u00f3n O, Blazquez-Herranz A, Torrente M, Rodr\u00edguez-Gonzalez A, Provencio M, Menasalvas E (2021) Extracting cancer treatments from clinical text written in Spanish: a deep learning approach. In: 2021 IEEE 8th International conference on Data Science and Advanced Analytics (DSAA), pp 1\u20136. IEEE","DOI":"10.1109\/DSAA53316.2021.9564137"},{"key":"3227_CR12","doi-asserted-by":"publisher","unstructured":"Najafabadipour M, Zanin M, Rodr\u00edguez-Gonz\u00e1lez A, Gonzalo-Mart\u00edn C, Nu\u00f1ez Garc\u00eda B, Calvo V, Luis Cruz Bermudez J, Provencio M, Menasalvas E (2019) Recognition of time expressions in Spanish electronic health records. In: 2019 IEEE 32nd International symposium on Computer-Based Medical Systems (CBMS), pp 69\u201374. https:\/\/doi.org\/10.1109\/CBMS.2019.00025","DOI":"10.1109\/CBMS.2019.00025"},{"key":"3227_CR13","doi-asserted-by":"publisher","unstructured":"Solarte-Pab\u00f3n O, Blazquez-Herranz A, Torrente M, Rodr\u00edguez-Gonzalez A, Provencio M, Menasalvas E (2021) Extracting cancer treatments from clinical text written in Spanish: a deep learning approach. In: 2021 IEEE 8th International conference on Data Science and Advanced Analytics (DSAA), pp 1\u20136. https:\/\/doi.org\/10.1109\/DSAA53316.2021.9564137","DOI":"10.1109\/DSAA53316.2021.9564137"},{"key":"3227_CR14","doi-asserted-by":"publisher","unstructured":"Solarte-Pab\u00f3n O, Torrente M, Garcia-Barrag\u00e1n A, Provencio M, Menasalvas E, Robles V (2022) Deep learning to extract breast cancer diagnosis concepts. In: 2022 IEEE 35th International symposium on Computer-Based Medical Systems (CBMS), pp 13\u201318. https:\/\/doi.org\/10.1109\/CBMS55023.2022.00010","DOI":"10.1109\/CBMS55023.2022.00010"},{"key":"3227_CR15","doi-asserted-by":"publisher","unstructured":"Santiso S, P\u00e9rez A, Casillas A, Oronoz M (2020) Neural negated entity recognition in Spanish electronic health records. J Biomed Inform 105 (December 2019):103419. https:\/\/doi.org\/10.1016\/j.jbi.2020.103419","DOI":"10.1016\/j.jbi.2020.103419"},{"key":"3227_CR16","doi-asserted-by":"publisher","first-page":"913","DOI":"10.7717\/peerj-cs.913","volume":"8","author":"OS Pab\u00f3n","year":"2022","unstructured":"Pab\u00f3n OS, Montenegro O, Torrente M, Gonz\u00e1lez AR, Provencio M, Menasalvas E (2022) Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach. PeerJ Comput Sci 8:913","journal-title":"PeerJ Comput Sci"},{"key":"3227_CR17","unstructured":"Miranda-Escalada A, Farr\u00e9 E, Krallinger M (2020) Named entity recognition, concept normalization and clinical coding: overview of the cantemist track for cancer text mining in Spanish, corpus, guidelines, methods and results. IberLEF@ SEPLN, 303\u2013323"},{"key":"3227_CR18","doi-asserted-by":"publisher","unstructured":"Neumann M, King D, Beltagy I, Ammar W (2019) ScispaCy: fast and robust models for biomedical natural language processing. In: Demner-Fushman D, Cohen KB, Ananiadou S, Tsujii J (eds) Proceedings of the 18th BioNLP workshop and shared task, pp 319\u2013327. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/W19-5034https:\/\/aclanthology.org\/W19-5034","DOI":"10.18653\/v1\/W19-5034"},{"key":"3227_CR19","doi-asserted-by":"publisher","unstructured":"Sakor A, Singh K, Patel A, Vidal M-E (2020) Falcon 2.0: an entity and relation linking tool over wikidata. In: Proceedings of the 29th ACM international conference on information; knowledge management. CIKM \u201920. ACM, Online. https:\/\/doi.org\/10.1145\/3340531.3412777","DOI":"10.1145\/3340531.3412777"},{"key":"3227_CR20","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. NAACL HLT 2019 - 2019 conference of the north american chapter of the association for computational linguistics: human language technologies - proceedings of the conference. 1(Mlm):4171\u20134186. arXiv:1810.04805"},{"key":"3227_CR21","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Barrag\u00e1n A, Solarte-Pab\u00f3n O, Nedostup G, Provencio M, Menasalvas E, Robles V (2023) Structuring breast cancer Spanish electronic health records using deep learning. In: 2023 IEEE 36th International symposium on Computer-Based Medical Systems (CBMS), pp 404\u2013409. IEEE","DOI":"10.1109\/CBMS58004.2023.00252"},{"key":"3227_CR22","doi-asserted-by":"publisher","unstructured":"Xiao Z, Tong H, Qu R, Xing H, Luo S, Zhu Z, Song F, Feng L (2023) Capmatch: semi-supervised contrastive transformer capsule with feature-based knowledge distillation for human activity recognition. IEEE Trans Neural Netw Learn Syst 1\u201315. https:\/\/doi.org\/10.1109\/TNNLS.2023.3344294","DOI":"10.1109\/TNNLS.2023.3344294"},{"issue":"4","key":"3227_CR23","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1109\/TCDS.2024.3370219","volume":"16","author":"Z Xiao","year":"2024","unstructured":"Xiao Z, Xu X, Xing H, Zhao B, Wang X, Song F, Qu R, Feng L (2024) DTCM: deep transformer capsule mutual distillation for multivariate time series classification. IEEE Trans Cognit Dev Syst 16(4):1445\u20131461. https:\/\/doi.org\/10.1109\/TCDS.2024.3370219","journal-title":"IEEE Trans Cognit Dev Syst"},{"issue":"1","key":"3227_CR24","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","volume":"34","author":"J Li","year":"2020","unstructured":"Li J, Sun A, Han J, Li C (2020) A survey on deep learning for named entity recognition. IEEE Trans Knowl Data Eng 34(1):50\u201370","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3227_CR25","doi-asserted-by":"crossref","unstructured":"Luo Y, Xiao F, Zhao H (2020) Hierarchical contextualized representation for named entity recognition. In: Proceedings of the AAAI conference on artificial intelligence 34:8441\u20138448","DOI":"10.1609\/aaai.v34i05.6363"},{"key":"3227_CR26","doi-asserted-by":"publisher","unstructured":"Baevski A, Edunov S, Liu Y, Zettlemoyer L, Auli M (2019) Cloze-driven pretraining of self-attention networks. In: Inui K, Jiang J, Ng V, Wan X (eds) 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 5360\u20135369. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1539https:\/\/aclanthology.org\/D19-1539","DOI":"10.18653\/v1\/D19-1539"},{"key":"3227_CR27","doi-asserted-by":"publisher","unstructured":"Jiang Y, Hu C, Xiao T, Zhang C, Zhu J (2019) Improved differentiable architecture search for language modeling and named entity recognition. In: Inui K, Jiang J, Ng V, Wan X (eds) 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 3585\u20133590. Asso-ciation for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1367https:\/\/aclanthology.org\/D19-1367","DOI":"10.18653\/v1\/D19-1367"},{"key":"3227_CR28","doi-asserted-by":"publisher","unstructured":"Lison P, Barnes J, Hubin A, Touileb S (2020) Named entity recognition without labelled data: a weak supervision approach. In: Jurafsky D, Chai J, Schluter N, Tetreault J (eds) Proceedings of the 58th annual meeting of the association for computational linguistics, pp 1518\u20131533. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.139https:\/\/aclanthology.org\/2020.acl-main.139","DOI":"10.18653\/v1\/2020.acl-main.139"},{"issue":"1","key":"3227_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"Y Kim","year":"2020","unstructured":"Kim Y, Lee JH, Choi S, Lee JM, Kim J-H, Seok J, Joo HJ (2020) Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records. Sci Rep 10(1):1\u20139","journal-title":"Sci Rep"},{"key":"3227_CR30","doi-asserted-by":"publisher","unstructured":"Martina S, Ventura L, Frasconi P (2020) Classification of cancer pathology reports: a large-scale comparative study. IEEE J Biomed Health Inform 24(11):3085\u20133094. https:\/\/doi.org\/10.1109\/JBHI.2020.3005016arXiv:2006.16370","DOI":"10.1109\/JBHI.2020.3005016"},{"key":"3227_CR31","doi-asserted-by":"crossref","unstructured":"Bitterman D, Chen\u00a0Lin H, Finan S, Warner J, Mak R, Savova G (2020) Extracting radiotherapy treatment details using neural network-based natural language processing. In: Annual meeting of the american society for radiation oncology, Cham","DOI":"10.1016\/j.ijrobp.2020.07.219"},{"key":"3227_CR32","doi-asserted-by":"publisher","first-page":"102625","DOI":"10.1016\/j.artmed.2023.102625","volume":"143","author":"O Solarte-Pab\u00f3n","year":"2023","unstructured":"Solarte-Pab\u00f3n O, Montenegro O, Garc\u00eda-Barrag\u00e1n A, Torrente M, Provencio M, Menasalvas E, Robles V (2023) Transformers for extracting breast cancer information from Spanish clinical narratives. Artif Intell Med 143:102625","journal-title":"Artif Intell Med"},{"issue":"3","key":"3227_CR33","doi-asserted-by":"publisher","first-page":"527","DOI":"10.3233\/SW-222986","volume":"13","author":"\u00d6 Sevgili","year":"2022","unstructured":"Sevgili \u00d6, Shelmanov A, Arkhipov M, Panchenko A, Biemann C (2022) Neural entity linking: a survey of models based on deep learning. Semantic Web 13(3):527\u2013570","journal-title":"Semantic Web"},{"key":"3227_CR34","doi-asserted-by":"publisher","unstructured":"Poerner N, Waltinger U, Sch\u00fctze H (2020) E-BERT: efficient-yet-effective entity embeddings for BERT. In: Cohn T, He Y, Liu Y (eds) Findings of the association for computational linguistics: EMNLP 2020, pp 803\u2013818. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.71https:\/\/aclanthology.org\/2020.findings-emnlp.71","DOI":"10.18653\/v1\/2020.findings-emnlp.71"},{"key":"3227_CR35","unstructured":"Cao ND, Izacard G, Riedel S, Petroni F (2021) Autoregressive entity retrieval. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=5k8F6UU39V"},{"key":"3227_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11964-9_29","author":"R Usbeck","year":"2014","unstructured":"Usbeck R, Ngonga Ngomo A-C, Auer S, Gerber D, Both A, Coelho S (2014). AGDISTIS - graph-based disambiguation of named entities using linked data. https:\/\/doi.org\/10.1007\/978-3-319-11964-9_29","journal-title":"AGDISTIS - graph-based disambiguation of named entities using linked data."},{"key":"3227_CR37","doi-asserted-by":"publisher","unstructured":"Onando M, Singh K, Vyas A, Shekarpour S, Vidal M-E, Auer S (2020) Encoding knowledge graph entity aliases in attentive neural network for wikidata entity linking, pp 328\u2013342. https:\/\/doi.org\/10.1007\/978-3-030-62005-9_24","DOI":"10.1007\/978-3-030-62005-9_24"},{"key":"3227_CR38","unstructured":"Cao ND, Izacard G, Riedel S, Petroni F (2021) Autoregressive entity retrieval"},{"key":"3227_CR39","doi-asserted-by":"crossref","unstructured":"Ayoola T, Tyagi S, Fisher J, Christodoulopoulos C, Pierleoni A (2022) ReFinED: an efficient zero-shot-capable approach to end-to-end entity linking","DOI":"10.18653\/v1\/2022.naacl-industry.24"},{"key":"3227_CR40","doi-asserted-by":"crossref","unstructured":"Le P, Titov I (2019) Distant learning for entity linking with automatic noise detection","DOI":"10.18653\/v1\/P19-1400"},{"key":"3227_CR41","doi-asserted-by":"crossref","unstructured":"Logeswaran L, Chang M-W, Lee K, Toutanova K, Devlin J, Lee H (2019) Zero-shot entity linking by reading entity descriptions","DOI":"10.18653\/v1\/P19-1335"},{"key":"3227_CR42","doi-asserted-by":"crossref","unstructured":"Wu L, Petroni F, Josifoski M, Riedel S, Zettlemoyer L (2020) Scalable zero-shot entity linking with dense entity retrieval","DOI":"10.18653\/v1\/2020.emnlp-main.519"},{"key":"3227_CR43","doi-asserted-by":"publisher","unstructured":"Hitzler P, Eberhart A, Ebrahimi M, Sarker MK, Zhou L (2022) Neuro-symbolic approaches in artificial intelligence. Nat Sci Rev 9(6):035. https:\/\/doi.org\/10.1093\/nsr\/nwac035https:\/\/academic.oup.com\/nsr\/article-pdf\/9\/6\/nwac035\/43952953\/nwac035.pdf","DOI":"10.1093\/nsr\/nwac035"},{"key":"3227_CR44","doi-asserted-by":"crossref","unstructured":"Sakor A, Mulang IO, Singh K, Shekarpour S, Vidal ME, Lehmann J, Auer S (2019) Old is gold: linguistic driven approach for entity and relation linking of short text. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 2336\u20132346","DOI":"10.18653\/v1\/N19-1243"},{"key":"3227_CR45","doi-asserted-by":"crossref","unstructured":"Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z (2007) DBpedia: a nucleus for a web of open data. In: Aberer K, Choi K-S, Noy N, Allemang D, Lee K-I, Nixon L, Golbeck J, Mika P, Maynard D, Mizoguchi R, Schreiber G, Cudr\u00e9-Mauroux P (eds) The Semantic Web. Springer, Berlin, Heidelberg, pp 722\u2013735","DOI":"10.1007\/978-3-540-76298-0_52"},{"key":"3227_CR46","doi-asserted-by":"publisher","unstructured":"Vrande\u010di\u0107 D (2012) Wikidata: a new platform for collaborative data collection. WWW \u201912 Companion, pp 1063\u20131064. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2187980.2188242","DOI":"10.1145\/2187980.2188242"},{"key":"3227_CR47","unstructured":"Delpeuch A (2020) OpenTapioca: lightweight entity linking for wikidata"},{"key":"3227_CR48","doi-asserted-by":"publisher","unstructured":"Jiang H, Gurajada S, Lu Q, Neelam S, Popa L, Sen P, Li Y, Gray A (2021) LNN-EL: a neuro-symbolic approach to short-text entity linking. In: Zong C, Xia F, Li W, Navigli R (eds) 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 775\u2013787. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.64https:\/\/aclanthology.org\/2021.acl-long.64","DOI":"10.18653\/v1\/2021.acl-long.64"},{"key":"3227_CR49","doi-asserted-by":"crossref","unstructured":"Plu J, Rizzo G, Troncy R (2015) A hybrid approach for entity recognition and linking. In: Semantic web evaluation challenges: second semwebeval challenge at ESWC 2015, Portoro\u017e, Slovenia, May 31-June 4, 2015, Revised Selected Papers, pp 28\u201339. Springer","DOI":"10.1007\/978-3-319-25518-7_3"},{"key":"3227_CR50","unstructured":"Ji Z, Wei Q, Xu H (2020) BERT-based ranking for biomedical entity normalization. AMIA summits on translational science proceedings 269"},{"key":"3227_CR51","doi-asserted-by":"crossref","unstructured":"Kalyan KS, Sangeetha S (2021) BertMCN: mapping colloquial phrases to standard medical concepts using BERT and highway network. Artif Intell Med 102008","DOI":"10.1016\/j.artmed.2021.102008"},{"key":"3227_CR52","doi-asserted-by":"publisher","unstructured":"Kalyan KS, Sangeetha S (2020) Medical concept normalization in user-generated texts by learning target concept embeddings. In: Holderness E, Jimeno\u00a0Yepes A, Lavelli A, Minard A-L, Pustejovsky J, Rinaldi F (eds) Proceedings of the 11th international workshop on health text mining and information analysis, pp 18\u201323. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.louhi-1.3https:\/\/aclanthology.org\/2020.louhi-1.3","DOI":"10.18653\/v1\/2020.louhi-1.3"},{"key":"3227_CR53","unstructured":"Pattisapu N, Patil S, Palshikar G, Varma V (2020) Medical concept normalization by encoding target knowledge. In: Machine learning for health workshop, pp 246\u2013259. PMLR"},{"key":"3227_CR54","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-031-63775-9_19","volume-title":"Computational Science \u2013 ICCS 2024","author":"F Gallego","year":"2024","unstructured":"Gallego F, L\u00f3pez-Garc\u00eda G, Gasco-S\u00e1nchez L, Krallinger M, Veredas FJ (2024) ClinLinker: medical entity linking of clinical concept mentions in Spanish. In: Franco L, Mulatier C, Paszynski M, Krzhizhanovskaya VV, Dongarra JJ, Sloot PMA (eds) Computational Science \u2013 ICCS 2024. Springer, Cham, pp 266\u2013280"},{"key":"3227_CR55","doi-asserted-by":"publisher","unstructured":"Duan S, Guang Y, Bu W, Yang J (2023) A survey of named entity disambiguation in entity linking. In: 2023 3rd International conference on Intelligent Communications and Computing (ICC), pp 296\u2013303. https:\/\/doi.org\/10.1109\/ICC59986.2023.10421092","DOI":"10.1109\/ICC59986.2023.10421092"},{"key":"3227_CR56","unstructured":"Bunescu R, Pasca M (2006) Using encyclopedic knowledge for named entity disambiguation. In: 11th Conference of the european chapter of the association for computational linguistics, pp 9\u201316"},{"key":"3227_CR57","doi-asserted-by":"crossref","unstructured":"Liu S, Fang Y (2023) Use large language models for named entity disambiguation in academic knowledge graphs. In: 2023 3rd International conference on Education, Information Management and Service Science (EIMSS 2023), pp 681\u2013691. Atlantis Press","DOI":"10.2991\/978-94-6463-264-4_79"},{"key":"3227_CR58","doi-asserted-by":"publisher","first-page":"101480","DOI":"10.1016\/j.softx.2023.101480","volume":"23","author":"R Kafando","year":"2023","unstructured":"Kafando R, Decoupes R, Roche M, Teisseire M (2023) SNEToolkit: spatial named entities disambiguation toolkit. SoftwareX. 23:101480","journal-title":"SoftwareX."},{"issue":"2","key":"3227_CR59","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s10115-021-01642-9","volume":"64","author":"W Bouarroudj","year":"2022","unstructured":"Bouarroudj W, Boufaida Z, Bellatreche L (2022) Named entity disambiguation in short texts over knowledge graphs. Knowl Inf Syst 64(2):325\u2013351","journal-title":"Knowl Inf Syst"},{"key":"3227_CR60","doi-asserted-by":"publisher","unstructured":"Varma M, Orr L, Wu S, Leszczynski M, Ling X, R\u00e9 C (2021) Cross-domain data integration for named entity disambiguation in biomedical text. In: Moens M-F, Huang X, Specia L, Yih SW-t (eds) Findings of the Association for Computational Linguistics: EMNLP 2021, pp 4566\u20134575. Association for Computational Linguistics, Punta Cana, Dominican Republic. https:\/\/doi.org\/10.18653\/v1\/2021.findings-emnlp.388https:\/\/aclanthology.org\/2021.findings-emnlp.388","DOI":"10.18653\/v1\/2021.findings-emnlp.388"},{"key":"3227_CR61","doi-asserted-by":"crossref","unstructured":"Wang X, Tsujii J, Ananiadou S (2009) Classifying relations for biomedical named entity disambiguation. In: Proceedings of the 2009 conference on empirical methods in natural language processing, pp 1513\u20131522","DOI":"10.3115\/1699648.1699698"},{"key":"3227_CR62","doi-asserted-by":"publisher","unstructured":"Vretinaris A, Lei C, Efthymiou V, Qin X, \u00d6zcan F (2021) Medical entity disambiguation using graph neural networks. Proceedings of the 2021 international conference on management of data. https:\/\/doi.org\/10.1145\/3448016.3457328","DOI":"10.1145\/3448016.3457328"},{"key":"3227_CR63","doi-asserted-by":"crossref","unstructured":"Garda S, Leser U (2024) BELHD: improving biomedical entity linking with homonoym disambiguation","DOI":"10.1093\/bioinformatics\/btae474"},{"key":"3227_CR64","doi-asserted-by":"crossref","unstructured":"Angell R, Monath N, Mohan S, Yadav N, McCallum A (2021) Clustering-based inference for biomedical entity linking","DOI":"10.18653\/v1\/2021.naacl-main.205"},{"key":"3227_CR65","doi-asserted-by":"crossref","unstructured":"Sung M, Jeon H, Lee J, Kang J (2020) Biomedical entity representations with synonym marginalization. In: Jurafsky D, Chai J, Schluter N, Tetreault J (eds) Proceedings of the 58th annual meeting of the association for computational linguistics, pp 3641\u20133650. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.335.https:\/\/aclanthology.org\/2020.acl-main.335","DOI":"10.18653\/v1\/2020.acl-main.335"},{"key":"3227_CR66","doi-asserted-by":"crossref","unstructured":"Logeswaran L, Chang M-W, Lee K, Toutanova K, Devlin J, Lee H (2019) Zero-shot entity linking by reading entity descriptions. In: Proceedings of the 57th annual meeting of the association for computational linguistics","DOI":"10.18653\/v1\/P19-1335"},{"issue":"14","key":"3227_CR67","doi-asserted-by":"publisher","first-page":"12657","DOI":"10.1609\/aaai.v35i14.17499","volume":"35","author":"L Chen","year":"2021","unstructured":"Chen L, Varoquaux G, Suchanek FM (2021) A lightweight neural model for biomedical entity linking. Proc AAAI Conf Artif Intell 35(14):12657\u201312665. https:\/\/doi.org\/10.1609\/aaai.v35i14.17499","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3227_CR68","doi-asserted-by":"crossref","unstructured":"Chen S, Wang J, Jiang F, Lin C-Y (2020) Improving entity linking by modeling latent entity type information. Proceedings of the AAAI conference on artificial intelligence 34:7529\u20137537","DOI":"10.1609\/aaai.v34i05.6251"},{"key":"3227_CR69","doi-asserted-by":"publisher","unstructured":"Garc\u00eda-Barrag\u00e1n A (2024). Breast-Norm-Benchmark. https:\/\/doi.org\/10.5281\/zenodo.12703934","DOI":"10.5281\/zenodo.12703934"},{"key":"3227_CR70","doi-asserted-by":"crossref","unstructured":"Bekkum M, Boer M, Harmelen F, Meyer-Vitali A, Teije A (2021) Modular design patterns for hybrid learning and reasoning systems. Appl Intell 51(9)","DOI":"10.1007\/s10489-021-02394-3"},{"key":"3227_CR71","doi-asserted-by":"crossref","unstructured":"Robertson S, Zaragoza H et al (2009) The probabilistic relevance framework: BM25 and beyond. Foundations and Trends\u00ae in Information Retrieval 3(4):333\u2013389","DOI":"10.1561\/1500000019"},{"key":"3227_CR72","unstructured":"Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, Chi E, Le Q, Zhou D (2023) Chain-of-thought prompting elicits reasoning in large language models"},{"key":"3227_CR73","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-0847-9_16","author":"S Doan","year":"2014","unstructured":"Doan S, Conway M, Phuong TM, Ohno-Machado L (2014) Natural language processing in biomedicine: a unified system architecture overview, pp 275\u2013294. Springer, New York, NY. https:\/\/doi.org\/10.1007\/978-1-4939-0847-9_16","journal-title":"Springer, New York, NY."},{"key":"3227_CR74","unstructured":"Labrak Y, Rouvier M, Dufour R (2023) A zero-shot and few-shot study of instruction-finetuned large language models applied to clinical and biomedical tasks"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03227-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03227-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03227-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T15:21:14Z","timestamp":1741620074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03227-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"references-count":74,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3227"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03227-4","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"31 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}