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Reseach Article

Semantic Method of Recovering of Medical Articles

by Jucélio Costa De Araújo, José Maria Parente De Oliveira, Leonardo Garcia Marques
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 16
Year of Publication: 2015
Authors: Jucélio Costa De Araújo, José Maria Parente De Oliveira, Leonardo Garcia Marques
10.5120/ijca2015906750

Jucélio Costa De Araújo, José Maria Parente De Oliveira, Leonardo Garcia Marques . Semantic Method of Recovering of Medical Articles. International Journal of Computer Applications. 128, 16 ( October 2015), 26-32. DOI=10.5120/ijca2015906750

@article{ 10.5120/ijca2015906750,
author = { Jucélio Costa De Araújo, José Maria Parente De Oliveira, Leonardo Garcia Marques },
title = { Semantic Method of Recovering of Medical Articles },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 16 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 26-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number16/22959-2015906750/ },
doi = { 10.5120/ijca2015906750 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:21:54.094585+05:30
%A Jucélio Costa De Araújo
%A José Maria Parente De Oliveira
%A Leonardo Garcia Marques
%T Semantic Method of Recovering of Medical Articles
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 16
%P 26-32
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The amount of information produced today reached an unprecedented contingent in history, which makes it difficult to locate relevant documents in a search. This situation leads to the need to use tools that facilitate the search, making information retrieval area increasingly important in the development of this context. Based on the organization of documents and ontology, information retrieval area has the challenge of getting smarter ways to recovery than just select syntactically documents, exploiting to the full the semantic context when selecting the information. This paper presents a semantic enrichment method that seeks to improve the quality of results when querying a database of medical articles. The proposed method performs a search on the repository of articles that is submitted to latent semantic analysis together with the National Cancer Institute (NCI) ontology and the lexical WordNet ontology database. After this joint treatment, the semantic relationship of those new terms to the survey conducted in the context is performed in order to improve the accuracy of recovery and enabling the retrieve of more relevant articles regarding the search.

References
  1. Yan, P., Jiao, Y., Hurson, A. R. e Potok, T. E. 2006. Semantic-based information retrieval of biomedical data. In Proceedings of the 2006 ACM Symposium on Applied Computing, New York, USA, pp 1700-1704.
  2. Liu, J. and Birnbaum, L. 2007. Measuring Semantic Similarity between Named Entities by Searching the Web Directory. In Proceedings of the IEEE/WIC/ACM international Conference on Web intelligence. Washington-DC, USA, pp 461-465.
  3. Soergel, D. 2005. Thesauri and ontologies in digital libraries. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries. New York, USA, p 421.
  4. Keleberda, I., Repka, V., e Biletskiy, Y. 2006. Building learner's ontologies to assist personalized search of learning objects. In Proceedings of the 8th international Conference on Electronic Commerce. Fredericton, New Brunswick, Canada, pp 569-573.
  5. Berners-Lee, T., Hendler, J. Lassila, O. The Semantic Web. Scientific American., n. 5, p. 29-37, 2001.
  6. Gruber, T., 1993. A translation approach to portable ontology specification. Knowledge Acquisition Journal, Vol 5, No. 2 pp 199-220.
  7. Finin, T., Ding L., Zou L. 2005.Social Networking on the Semantic Web-University of Maryland, Baltimore County Baltimore MD USA.
  8. Breitman, K.K. 2005. Web Semântica: a Internet do futuro. Editota LTC. Rio de Janeiro, Brasil.
  9. Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K., 1990. Introduction to WordNet: An On-line lexical Database. International Journal of Lexicography, 3(4), pp. 235-244.
  10. Richardson, R., Smeaton, A. F., 1995. Using WordNet in a Knowledge-Based Approach to Information Retrieval. University of Dublin technical report CA-0395, Dublin, Ireland
  11. Smeaton, A. F., Quigley, I., 1996. Experiments on Using Semantic Distances Between Words in Image Caption Retrieval. University of Dublin technical report CA-0196, Dublin, Ireland.
  12. Gómez-Hidalgo, J.M. and Rodriguez, M.B. 1997. Integrating a Lexical Database and a Training Collection for Text Categorization. ACL/EACL Workshop on Automatic Extraction of Lexical Semantic Resources for NL Applications. Madrid, Spain, pp. 39-44.
  13. Miller, G. A., 1985. ‘Wordnet: A Dictionary Browser’ in Information in Data, Proceedings of the First Conference of the UW Centre for the New Oxford Dictionary. University of Waterloo. Waterloo, Canada.
  14. Grossman, D. A.; Frieder, O. 2004. Information Retrieval: Algorithms and Heuristics. [S.l.]: Dordrecht, Netherlands : Springer, 272p.
  15. Salton,G.M., McGill, M.J. 1983. "Introduction to Modern Information Retrieval." McGraw-Hill, Michigan,USA, 1983 ISBN 0070544840, 1983
  16. Shortliffe, E. H., Barnet, G. O. 2001. Medical data: Their acquisition, storage and use. In: Shortliffe, E. H.; Perreault, L. E.; Wiederhold, G.; Fagan, L. M (Ed.). Medical informatics computer applications in health care and biomedicine. 2nd ed. New York: Springer.
  17. Lin, K. H.-Y.; Hou, W.-J.; Chen, H.-H. 2005. Retrieval of Biomedical Documents by Prioritizing Key Phrases In: Proceedings of the 14th Text REtrieval Conference, Gaithersburg, Maryland.
  18. Mykowiecka, A.; Marciniak, M.; Kupsc, A. 2009. Rule-based information extraction from patients' clinical data. J. of Biomedical Informatics, v. 42, n. 5, p. 923-936.
  19. Lourenço, A.; Carreira, R.; Glez-Peña, D; Méndez, J. R.; Carneiro, S.; Rocha, L. M.; Díaz, F.; Ferreira, E. C.; Rocha, I.; Fdez-Riverola, F.; Rocha, M. 2010. BioDR: Semanticindexing networks for biomedical document retrieval. Expert Systems with Applications, v. 37, n. 4, p. 3444-3453.
  20. Thakker, D. et al., 2012.Taming Digital Traces for Informal Learning: A Semantic-driven Approach., Berlin, Heidelberg. Anais. Berlin, Heidelberg: Springer-Verlag, pp.348–362.
  21. Baldan, M. A., Menezes, C. S. 2012. Um Ambiente para Construção de Perfis a Partir de Textos Pessoais. Anais do Simpósio Brasileiro de Informática na Educação, v. 23, n. 1.
  22. Zapater, J. J. S., Mendes Neto, F. M., 2014. Uso de tecnologías semánticas en diferentes dominios de aplicación: Entorno educativo y sistemas de información de tráfico vial. Saarbrücken: Editorial Académica Española, Madrid,Espanha.
  23. Sheth, A., Arpinar, I. B., Kashyap, V., 2003. Relationships at the heart of semantic web: Modeling, discovering, and exploiting complex semantic relationships. SpringerVerlag, p p. 63–94.
  24. Landauner ,T., Foltz W. P., Laham D.,1998. "Introduction to Latent Semantic Analysis", Lawrence Erlbaum Associates, New Jersey, USA.
  25. Golub, Gene H.; Van Loan, Charles F., 1996. In: Gene H.. Matrix Computations. Vol 3, JHU Press, Baltimore, USA.
  26. Novelli, A. D. P.; Oliveira, J. Simple method for ontology automatic extraction from documents. International Journal of Advanced Computer Science and Applications, v. 3, p. 44-51, 2012.
  27. Manning, C.; Raghavan P.; Schutze, H.An Introduction to Information Retrieval. New York: Cambridge University Press, 2009
  28. Maia, L. G.; Souza, R. R. Medidas de similaridade entre documentos eletrônicos. IX ENACIB, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Information Retrieval Semantic Enrichment Ontology Comparison by Similarity.