Linked Life Data

19497938

Source:http://linkedlifedata.com/resource/pubmed/id/19497938

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
15
pubmed:dateCreated
2009-7-20
pubmed:abstractText
Clustering MEDLINE documents is usually conducted by the vector space model, which computes the content similarity between two documents by basically using the inner-product of their word vectors. Recently, the semantic information of MeSH (Medical Subject Headings) thesaurus is being applied to clustering MEDLINE documents by mapping documents into MeSH concept vectors to be clustered. However, current approaches of using MeSH thesaurus have two serious limitations: first, important semantic information may be lost when generating MeSH concept vectors, and second, the content information of the original text has been discarded.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1944-51
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity.
pubmed:affiliation
Shanghai Key Lab of Intelligent Information Processing, Fudan University, School of Computer Science, Fudan University, Shanghai 200433, China. zhushanfeng@gmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't