Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


December Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the December 2021 Edition of the journal. The last date of research paper submission is November 15, 2021.

Using Concept Definitions and Ontology Structure to Measure Semantic Similarity in Biomedicine

Olivia Sanchez Graillet Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
Download full text
  1. Olivia Sanchez Graillet. Article: Using Concept Definitions and Ontology Structure to Measure Semantic Similarity in Biomedicine. International Journal of Applied Information Systems 7(6):1-5, July 2014. BibTeX

    	author = "Olivia Sanchez Graillet",
    	title = "Article: Using Concept Definitions and Ontology Structure to Measure Semantic Similarity in Biomedicine",
    	journal = "International Journal of Applied Information Systems",
    	year = 2014,
    	volume = 7,
    	number = 6,
    	pages = "1-5",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"


Semantic similarity is useful in different areas of Natural Language Processing (NLP), such as word-sense disambiguation and nameentity recognition, as well as in information retrieval. On the other hand, specialised NLP tools are required in the biomedical context due to the huge amount of information currently available in digital publications that can be explored. This paper presents a method for calculating the semantic similarity between pairs of biomedical concepts defined in an ontology derived from the SNOMED-CT vocabulary. A final semantic similarity is obtained by calculating the similarity between the components of the two concept definitions based on their shared and unshared ancestors in the ontology hierarchy. The results are compared with other methods as well as with human expert ranks as baseline.


  1. T. Pedersen, S. V. Pakhomov, S. Patwardhan, and C. G. Chute. Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics, 40(3):288–299, 2007.
  2. A. Budanitsky and G. Hirst. Evaluating wordnetbased measures of semantic relatedness. Computational Linguistics, 32(1):13–47, 2006.
  3. S. Patwardhan, S. Banerjee, and T. Pedersen. Using measures of semantic relatedness for word sense disambiguation. In Proceedings of the Forth International Conference on Computational Linguistics and Intelligent Text Processing, CICLing'03, pages 241–257, Mexico City, Mexico, 2003.
  4. L. Kobyli´nski and M. Kope´c. Semantic similarity functions in word sense disambiguation. In Text, Speech and Dialogue, pages 31–38. Springer, 2012.
  5. T. Grego and F. M. Couto. Enhancement of chemical entity identification in text using semantic similarity validation. PLoS ONE, 8(5), 2013.
  6. A. Hliaoutakis, G. Varelas, E. Voutsakis, E. G. M. Petrakis, and E. Milios. Information retrieval by semantic similarity. Int. J. Semantic Web Inf. Syst. (IJSWIS), 2(3):55–73, 2006.
  7. G. Varelas, E. Voutsakis, E. G. M. Petrakis, E. E. Milios, and P. Raftopoulou. Semantic similarity methods in wordnet and their application to information retrieval on the web. In 7th ACM International Workshop on Web Information and Data Management (WIDM), pages 10–16. ACM Press, 2005.
  8. P. Atzeni, F. Polticelli, and D. Toti. Knowledge discovery from textual sources by using semantic similarity. In 20th Italian Symposium on Advanced Database Systems (SEBD), pages 213–220. ACM Press, 2012.
  9. R. Rada, H. Mili, E. Bicknell, and M. Blettner. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics, 19(1):17–30, 1989.
  10. Z. Wu and M. Palmer. Verbs semantics and lexical selection. In Proceedings of the 32nd annual meeting on Association for Computational Linguistics, ACL '94, pages 133–138, Stroudsburg, PA, USA, 1994. Association for Computational Linguistics.
  11. J. J. Jiang and D. W. Conrath. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics, pages 19–33, 1997.
  12. Y. Li, Z. A. Bandar, and D. McLean. An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans. on Knowl. and Data Eng. , 15(4):871–882, 2003.
  13. I. Spasic and S. Ananiadou. A flexible measure of contextual similarity for biomedical terms. In Pacific Biocomputing Symposium, pages 197–208, 2005.
  14. M. Batet, D. S´anchez, and A. Valls. An ontology-based measure to compute semantic similarity in biomedicine. Journal of Biomedical Informatics, 44(1):118–125, 2011.
  15. C. Leacock and M. Chodorow. Combining local context and WordNet similarity for word sense identification, pages 305–332. In C. Fellbaum (Ed. ), MIT Press, 1998.
  16. P. Resnik. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 448–453, 1995.
  17. D. Lin. An information-theoretic definition of similarity. In Proceedings of the Fifteenth International Conference on Machine Learning, ICML '98, pages 296–304, San Francisco, CA, USA, 1998. Morgan Kaufmann Publishers Inc.
  18. H. Al-Mubaid and H. A. Nguyen. A cluster-based approach for semantic similarity in the biomedical domain. In Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE, pages 2713–17, 2006.
  19. S. Patwardhan and T. Pedersen. Using wordnet-based context vectors to estimate the semantic relatedness of concepts. In Proceedings of the EACL 2006 workshop, making sense of sense: Bringing computational linguistics and psycholinguistics together, pages 1–8, 2006.
  20. G. Wade. SNOMED CT: The Clinical Data Standard. Overview and Application to eHRs, 2013.


Semantic similarity, Knowledge discovery, Biomedical ontologies, Knowledge representation