Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms. However, most treat ontologies as simple terminologies, without making use of the ontology structure or the semantic similarity between terms. Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively.
:D\:\\ASUS-FINO\\Inkhognito_M\\David\\PhD\\Web Semântica\\Biomedicine Ontology\\2009-BMC-Biomedical word sense disambiguation with ontologies and metadata - automation meets accuracy..pdf:pdf
%0 Journal Article
%1 Alexopoulou2009
%A Alexopoulou, Dimitra
%A Andreopoulos, Bill
%A Dietze, Heiko
%A Doms, Andreas
%A Gandon, Fabien
%A Hakenberg, Jörg
%A Khelif, Khaled
%A Schroeder, Michael
%A Wächter, Thomas
%D 2009
%J BMC bioinformatics
%K Algorithms,Computational Automated,Unified Biology,Computational Biology: Controlled, Headings,Pattern Informatics,Medical Informatics: Language Medical NLP Recognition, Retrieval,Medical Storage Subject System,Vocabulary, and methods,Information methods,Medical
%P 28
%R 10.1186/1471-2105-10-28
%T Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy.
%U http://www.pubmedcentral.nih.gov/
%V 10
%X Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms. However, most treat ontologies as simple terminologies, without making use of the ontology structure or the semantic similarity between terms. Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively.
%@ 1471210510
@article{Alexopoulou2009,
abstract = {Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms. However, most treat ontologies as simple terminologies, without making use of the ontology structure or the semantic similarity between terms. Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively.},
added-at = {2015-01-14T21:25:14.000+0100},
author = {Alexopoulou, Dimitra and Andreopoulos, Bill and Dietze, Heiko and Doms, Andreas and Gandon, Fabien and Hakenberg, J\"{o}rg and Khelif, Khaled and Schroeder, Michael and W\"{a}chter, Thomas},
biburl = {https://www.bibsonomy.org/bibtex/21bbdd268287fbc02390f98e1f2004fce/diverzulu},
doi = {10.1186/1471-2105-10-28},
file = {:D\:\\ASUS-FINO\\Inkhognito_M\\David\\PhD\\Web Semântica\\Biomedicine Ontology\\2009-BMC-Biomedical word sense disambiguation with ontologies and metadata - automation meets accuracy..pdf:pdf},
interhash = {ea23e1d5859a057578c6c4a63de581fc},
intrahash = {1bbdd268287fbc02390f98e1f2004fce},
isbn = {1471210510},
issn = {1471-2105},
journal = {BMC bioinformatics},
keywords = {Algorithms,Computational Automated,Unified Biology,Computational Biology: Controlled, Headings,Pattern Informatics,Medical Informatics: Language Medical NLP Recognition, Retrieval,Medical Storage Subject System,Vocabulary, and methods,Information methods,Medical},
month = jan,
pages = 28,
pmid = {19159460},
timestamp = {2015-01-14T21:25:14.000+0100},
title = {Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy.},
url = {http://www.pubmedcentral.nih.gov/},
volume = 10,
year = 2009
}