BioDR: Semantic indexing networks for biomedical document retrieval

  • Authors:

  • Anália Lourenço;Rafael Carreira;Daniel Glez-Peña;José R. Méndez;Sónia Carneiro;Luis M. Rocha;Fernando Díaz;Eugénio C. Ferreira;Isabel Rocha;Florentino Fdez-Riverola;Miguel Rocha

  • Affiliations:

  • IBB - Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal;Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal;Computer Science Department, University of Vigo, ESEI: Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain;Computer Science Department, University of Vigo, ESEI: Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain;IBB - Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal;School of Informatics and Computing, Indiana University, 919 East Tenth Street, Bloomington, IN 47406, United States and Instituto Gulbenkian de a Ciência, Apartado 14, 2781-901 Oeiras, Portu ...;Computer Science Department, University of Valladolid, Escuela Universitaria de Informática, Plaza Santa Eulalia, 9-11, 40005 Segovia, Spain;IBB - Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal;IBB - Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal;Computer Science Department, University of Vigo, ESEI: Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain;Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

In Biomedical research, retrieving documents that match an interesting query is a task performed quite frequently. Typically, the set of obtained results is extensive containing many non-interesting documents and consists in a flat list, i.e., not organized or indexed in any way. This work proposes BioDR, a novel approach that allows the semantic indexing of the results of a query, by identifying relevant terms in the documents. These terms emerge from a process of Named Entity Recognition that annotates occurrences of biological terms (e.g. genes or proteins) in abstracts or full-texts. The system is based on a learning process that builds an Enhanced Instance Retrieval Network (EIRN) from a set of manually classified documents, regarding their relevance to a given problem. The resulting EIRN implements the semantic indexing of documents and terms, allowing for enhanced navigation and visualization tools, as well as the assessment of relevance for new documents.