Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI) |
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| Authors: | Dietrich Rebholz-Schuhmann Jee-Hyub Kim Ying Yan Abhishek Dixit Caroline Friteyre Robert Hoehndorf Rolf Backofen Ian Lewin |
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| Institution: | 1. Department of Computational Linguistics, University of Zürich, Zürich, Switzerland.; 2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.; 3. Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom.; 4. Albert-Ludwigs-University Freiburg, Fahnenbergplatz, Freiburg, Germany.; University of Illinois-Chicago, United States of America, |
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| Abstract: | MotivationBiomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical “term space” (the “Lexeome”), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness).ResultThis study compiles a resource for lexical terms of biomedical interest in a standard format (called “LexEBI”), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions.ConclusionLexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open source content, and fully interlinks terms across resources. |
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