Paper Info
Title
Ontology-based MEDLINE document classification
Abstract
An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents.
Year
DOI
Venue
2007
10.1007/978-3-540-71233-6_34
BIRD
Keywords
Field
DocType
document triage task,biomedical information,medline document,extension method,domain-independent method,medline indexer,information search,biomedical article,ontology-based medline document classification,mesh ontology inter-concept relationship,automatic information processing,subject headings,indexation,information processing,information retrieval
Semantic similarity,Ontology (information science),Document classification,Ontology,Information processing,Extension method,Information retrieval,Computer science,Text Retrieval Conference,MEDLINE
Conference
Volume
ISSN
Citations 
4414
0302-9743
9
PageRank 
References 
Authors
0.53
13
3
Name
Order
Citations
PageRank
Fabrice Camous1384.26
Stephen Blott21002168.03
Alan F. Smeaton34656518.60