title: The BioLexicon: a large-scale terminological resource for biomedical text mining.
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.
authors: School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. paul.thompson@manchester.ac.uk
authors: Paul
authors: P
authors: Thompson
authors: John
authors: J
authors: McNaught
authors: Simonetta
authors: S
authors: Montemagni
authors: Nicoletta
authors: N
authors: Calzolari
authors: Riccardo
authors: R
authors: del Gratta
authors: Vivian
authors: V
authors: Lee
authors: Simone
authors: S
authors: Marchi
authors: Monica
authors: M
authors: Monachini
authors: Piotr
authors: P
authors: Pezik
authors: Valeria
authors: V
authors: Quochi
authors: C J
authors: CJ
authors: Rupp
authors: Yutaka
authors: Y
authors: Sasaki
authors: Giulia
authors: G
authors: Venturi
authors: Dietrich
authors: D
authors: Rebholz-Schuhmann
authors: Sophia
authors: S
authors: Ananiadou
mesh_terms: D019295:Computational Biology; D057225:Data Mining; D016208:Databases, Factual; D006801:Humans; D012660:Semantics; D018875:Vocabulary, Controlled