Skip to main content
SpringerLink
Log in

Deep Semantic Entity Linking

  • Conference paper
  • First Online:
Book cover Advances in Information Retrieval (ECIR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12657))

Included in the following conference series:

  • 2253 Accesses

Abstract

Named entity linking systems are an essential component in text mining pipelines, mapping entity mentions in the text to the appropriate knowledge base identifiers. However, the current systems have several limitations affecting their performance: the lack of context of the entity mentions, the incomplete disambiguation graphs and the lack of approaches to deal with unlinkable entity mentions. The PhD project will focus on solving the aforementioned challenges in order to develop a NEL model which outperforms state-of-the-art performance in Biomedical and Life Sciences domains.

Supported by FCT through the DeST: Deep SemanticTagger project, ref. PTDC/CCI-BIO/28685/2017, PhD Scholarship, ref. 2020.05393.BD, LASIGE ResearchUnit, ref. UIDB/00408/2020 and ref. UIDP/00408/2020.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://scielo.org/.

  2. 2.

    https://pubmed.ncbi.nlm.nih.gov/.

References

  1. Alsentzer, E., et al.: Publicly available clinical BERT embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 72–78. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/w19-1909

  2. Ide, N., Pustejovsky, J. (eds.): Handbook of Linguistic Annotation. Springer, Dordrecht (2017). https://doi.org/10.1007/978-94-024-0881-2

    Book  MATH  Google Scholar 

  3. Couto, F.M., Lamurias, A.: MER: a shell script and annotation server for minimal named entity recognition and linking. J. Cheminform. 10(1), 58 (2018). https://doi.org/10.1186/s13321-018-0312-9

  4. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, October 2018. http://arxiv.org/abs/1810.04805

  5. Doǧan, R.I., Leaman, R., Lu, Z.: NCBI disease corpus: a resource for disease name recognition and concept normalization. J. Biomed. Inform. 47, 1–10 (2014). https://doi.org/10.1016/j.jbi.2013.12.006

    Article  Google Scholar 

  6. Dredze, M., Mcnamee, P., Rao, D., Gerber, A., Finin, T.: Entity disambiguation for knowledge base population. In: 23rd International Conference on Computational Linguistics, pp. 277–285, August 2010. https://doi.org/10.3115/1119176.1119181

  7. Guo, Z., Barbosa, D.: Robust named entity disambiguation with random walks. Semantic Web 9(4), 459–479 (2018). https://doi.org/10.3233/SW-170273

    Article  Google Scholar 

  8. Ji, Z., Wei, Q., Xu, H.: BERT-based Ranking for Biomedical Entity Normalization (2019). http://arxiv.org/abs/1908.03548

  9. Lamurias, A., Ruas, P., Couto, F.M.: PPR-SSM: personalized PageRank and semantic similarity measures for entity linking. BMC Bioinform. 20(1), 1–12 (2019). https://doi.org/10.1186/s12859-019-3157-y

    Article  Google Scholar 

  10. Lamurias, A., Sousa, D., Clarke, L.A., Couto, F.M.: BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies. BMC Bioinform. 20(10) (2019). https://doi.org/10.1186/s12859-018-2584-5

  11. Leaman, R., Khare, R., Lu, Z.: Challenges in clinical natural language processing for automated disorder normalization. J. Biomed. Inform. 57, 28–37 (2015). https://doi.org/10.1016/j.jbi.2015.07.010

  12. Leaman, R., Lu, Z.: TaggerOne: joint named entity recognition and normalization with semi-Markov Models. Bioinformatics 32(18), 2839–2846 (2016). https://doi.org/10.1093/bioinformatics/btw343

    Article  Google Scholar 

  13. Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 1–7 (2019). https://doi.org/10.1093/bioinformatics/btz682

  14. Li, J., et al.: BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database J. Biol. Databases Curation 2016, 1–10 (2016). https://doi.org/10.1093/database/baw068

  15. Meij, E., Balog, K., Odijk, D.: Entity linking and retrieval for semantic search. In: WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining, New York, New York, USA, February 2014, p. 683 (2014). https://doi.org/10.1145/2556195.2556201

  16. Mohan, S., Li, D.: Medmentions: a large biomedical corpus annotated with UMLS concepts (2019)

    Google Scholar 

  17. Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep Contextualized Word Representations, pp. 2227–2237 (2018). https://doi.org/10.18653/v1/n18-1202

  18. Qi, F., Huang, J., Yang, C., Liu, Z., Chen, X., Liu, Q., Sun, M.: Modeling semantic compositionality with sememe knowledge. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, July 28 - August 2, 2019, pp. 5706–5715. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/p19-1571

  19. Ruas, P., Lamurias, A., Couto, F.M.: Linking chemical and disease entities to ontologies by integrating PageRank with extracted relations from literature. J. Cheminform. 12(1), 1–11 (2020). https://doi.org/10.1186/s13321-020-00461-4

  20. Ruas, P., Lamúrias, A., Couto, F.M.: Towards a multilingual corpus for named entity linking evaluation in the clinical domain. In: CEUR Workshop Proceedings, vol. 2619, pp. 2–4 (2020)

    Google Scholar 

  21. Shen, W., Wang, J., Han, J.: Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans. Knowl. Data Eng. 27(2), 443–460 (2015). https://doi.org/10.1109/TKDE.2014.2327028. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6823700

  22. Sorokin, D., Gurevych, I.: Mixing context granularities for improved entity linking on question answering data across entity categories. In: 7th Joint Conference on Lexical and Computational Semantics (*SEM), pp. 65–75. Association for Computational Linguistics (2018)

    Google Scholar 

  23. Wu, G., He, Y., Hu, X.: Entity linking: an issue to extract corresponding entity with knowledge base. IEEE Access 6(c), 6220–6231 (2018). https://doi.org/10.1109/ACCESS.2017.2787787. http://ieeexplore.ieee.org/document/8246707/

Download references

Author information

Authors and Affiliations

  1. LASIGE, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisbon, Portugal

    Pedro Ruas

Authors
  1. Pedro Ruas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro Ruas .

Editor information

Editors and Affiliations

  1. Radboud University Nijmegen, Nijmegen, The Netherlands

    Djoerd Hiemstra

  2. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  3. Toulouse, Toulouse Institute of Computer Science Research, Toulouse, France

    Josiane Mothe

  4. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Raffaele Perego

  5. Leipzig University, Leipzig, Germany

    Martin Potthast

  6. Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Fabrizio Sebastiani

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ruas, P. (2021). Deep Semantic Entity Linking. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_81

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72240-1_81

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72239-5

  • Online ISBN: 978-3-030-72240-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation