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Istituto di linguistica computazionale "Antonio Zampolli"

Torna all'elenco Contributi in rivista anno 2011

Contributo in rivista

Tipo: Articolo in rivista

Titolo: The BioLexicon: a large-scale terminological resource for biomedical text mining

Anno di pubblicazione: 2011

Formato: Elettronico

Autori: Paul Thompson, John McNaught, Simonetta Montemagni, Nicoletta Calzolari, Riccardo del Gratta, Vivian Lee, Simone Marchi, Monica Monachini, Piotr Pezik, Valeria Quochi, CJ Rupp, Yutaka Sasaki, Giulia Venturi, Dietrich Rebholz-Schuhmann, Sophia Ananiadou

Affiliazioni autori: School of Computer Science, University of Manchester; National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester; Manchester Interdisciplinary Biocentre, University of Manchester; Istituto di Linguistica Computazionale del CNR; European Bioinformatics Institute, Wellcome Trust Genome Campus; Toyota Technological Institute

Autori CNR:


Lingua: inglese

Abstract: Background Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events. Results This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard. Conclusions The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.

Lingua abstract: inglese

Pagine da: 1

Pagine a: 29

Pagine totali: 29


BMC bioinformatics BioMed Central,
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1471-2105

Numero volume: 12

Numero fascicolo: 397

DOI: 10.1186/1471-2105-12-397

Referee: Sė: Internazionale

Stato della pubblicazione: Published version

Indicizzato da:

  • Scopus [2-s2.0-80053915290]
  • ISI Web of Science (WOS) [000297641800001]

Parole chiave:

  • Text Mining
  • Information Extraction
  • Computational Lexicon

URL: http://www.biomedcentral.com/1471-2105/12/397

Altre informazioni: ID_PUMA: cnr.ilc/2011-A0-011

Strutture CNR:


Allegati: The BioLexicon: a large-scale terminological resource for biomedical text mining (application/pdf)

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