BioDR : semantic indexing networks for biomedical document retrieval

• Author(s): Lourenço, Anália ; Carreira, Rafael ; Glez-Peña, Daniel ; Méndez, José R. ; Carneiro, S. ; Rocha, Luís M. ; Díaz, Fernando ; Ferreira, E. C. ; Rocha, I. ; Fdez-Riverola, Florentino ; Rocha, Miguel
• Date: 2010
• Persistent ID: http://hdl.handle.net/1822/10213
• Origin: RepositóriUM - Universidade do Minho
• Subject(s): Biomedical document retrieval; Document relevance; Enhanced Instance Retrieval Network; Named entity recognition; Semantic indexing document network
• Document Type: Article
• Language: English

Description

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.