Linked Life Data

20647054

Source:http://linkedlifedata.com/resource/pubmed/id/20647054

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2011-3-1
pubmed:abstractText
While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they must achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships as well as difficulty in updating the ontology as knowledge changes. Methodologies developed in the fields of Natural Language Processing, information extraction, information retrieval and machine learning provide techniques for automating the enrichment of an ontology from free-text documents. In this article, we review existing methodologies and developed systems, and discuss how existing methods can benefit the development of biomedical ontologies.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1532-0480
pubmed:author
pubmed:copyrightInfo
Published by Elsevier Inc.
pubmed:issnType
Electronic
pubmed:volume
44
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
163-79
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Natural Language Processing methods and systems for biomedical ontology learning.
pubmed:affiliation
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, PA 15232, USA.
pubmed:publicationType
Journal Article, Review, Research Support, N.I.H., Extramural