Integration and publication of heterogeneous text-mined relationships on the Semantic Web.
Identifieur interne : 000099 ( PubMed/Corpus ); précédent : 000098; suivant : 000100Integration and publication of heterogeneous text-mined relationships on the Semantic Web.
Auteurs : Adrien Coulet ; Yael Garten ; Michel Dumontier ; Russ B. Altman ; Mark A. Musen ; Nigam H. ShahSource :
- Journal of biomedical semantics [ 2041-1480 ] ; 2011.
Abstract
Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering.
DOI: 10.1186/2041-1480-2-S2-S10
PubMed: 21624156
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Altman</name></author><author><name sortKey="Musen, Mark A" sort="Musen, Mark A" uniqKey="Musen M" first="Mark A" last="Musen">Mark A. Musen</name></author><author><name sortKey="Shah, Nigam H" sort="Shah, Nigam H" uniqKey="Shah N" first="Nigam H" last="Shah">Nigam H. Shah</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2011">2011</date><idno type="doi">10.1186/2041-1480-2-S2-S10</idno><idno type="RBID">pubmed:21624156</idno><idno type="pmid">21624156</idno><idno type="wicri:Area/PubMed/Corpus">000099</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000099</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Integration and publication of heterogeneous text-mined relationships on the Semantic Web.</title><author><name sortKey="Coulet, Adrien" sort="Coulet, Adrien" uniqKey="Coulet A" first="Adrien" last="Coulet">Adrien Coulet</name><affiliation><nlm:affiliation>LORIA - INRIA Nancy - Grand-Est, Campus Scientifique - BP 239 - 54506 Vandoeuvre-lès-Nancy Cedex, France. adrien.coulet@loria.fr.</nlm:affiliation></affiliation></author><author><name sortKey="Garten, Yael" sort="Garten, Yael" uniqKey="Garten Y" first="Yael" last="Garten">Yael Garten</name></author><author><name sortKey="Dumontier, Michel" sort="Dumontier, Michel" uniqKey="Dumontier M" first="Michel" last="Dumontier">Michel Dumontier</name></author><author><name sortKey="Altman, Russ B" sort="Altman, Russ B" uniqKey="Altman R" first="Russ B" last="Altman">Russ B. Altman</name></author><author><name sortKey="Musen, Mark A" sort="Musen, Mark A" uniqKey="Musen M" first="Mark A" last="Musen">Mark A. Musen</name></author><author><name sortKey="Shah, Nigam H" sort="Shah, Nigam H" uniqKey="Shah N" first="Nigam H" last="Shah">Nigam H. Shah</name></author></analytic><series><title level="j">Journal of biomedical semantics</title><idno type="eISSN">2041-1480</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="PubMed-not-MEDLINE"><PMID Version="1">21624156</PMID><DateCreated><Year>2011</Year><Month>05</Month><Day>31</Day></DateCreated><DateCompleted><Year>2011</Year><Month>07</Month><Day>14</Day></DateCompleted><DateRevised><Year>2011</Year><Month>07</Month><Day>28</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2041-1480</ISSN><JournalIssue CitedMedium="Internet"><Volume>2 Suppl 2</Volume><PubDate><Year>2011</Year></PubDate></JournalIssue><Title>Journal of biomedical semantics</Title><ISOAbbreviation>J Biomed Semantics</ISOAbbreviation></Journal><ArticleTitle>Integration and publication of heterogeneous text-mined relationships on the Semantic Web.</ArticleTitle><Pagination><MedlinePgn>S10</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/2041-1480-2-S2-S10</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">We report on the semi-automatic construction of the PHARE relationship ontology (the PHArmacogenomic RElationships Ontology) consisting of 200 curated relations from over 40,000 heterogeneous relationships extracted via text-mining. These heterogeneous relations are then mapped to the PHARE ontology using synonyms, entity descriptions and hierarchies of entities and roles. Once mapped, relationships can be normalized and compared using the structure of the ontology to identify relationships that have similar semantics but different syntax. We compare and contrast the manual procedure with a fully automated approach using WordNet to quantify the degree of integration enabled by iterative curation and refinement of the PHARE ontology. The result of such integration is a repository of normalized biomedical relationships, named PHARE-KB, which can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The PHARE ontology serves as a common semantic framework to integrate more than 40,000 relationships pertinent to pharmacogenomics. The PHARE ontology forms the foundation of a knowledge base named PHARE-KB. Once populated with relationships, PHARE-KB (i) can be visualized in the form of a biological network to guide human tasks such as database curation and (ii) can be queried programmatically to guide bioinformatics applications such as the prediction of molecular interactions. PHARE is available at http://purl.bioontology.org/ontology/PHARE.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Coulet</LastName><ForeName>Adrien</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>LORIA - INRIA Nancy - Grand-Est, Campus Scientifique - BP 239 - 54506 Vandoeuvre-lès-Nancy Cedex, France. adrien.coulet@loria.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Garten</LastName><ForeName>Yael</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Dumontier</LastName><ForeName>Michel</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Altman</LastName><ForeName>Russ B</ForeName><Initials>RB</Initials></Author><Author ValidYN="Y"><LastName>Musen</LastName><ForeName>Mark A</ForeName><Initials>MA</Initials></Author><Author ValidYN="Y"><LastName>Shah</LastName><ForeName>Nigam H</ForeName><Initials>NH</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2011</Year><Month>05</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Biomed Semantics</MedlineTA><NlmUniqueID>101531992</NlmUniqueID></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>BMC Bioinformatics. 2008;9:78</RefSource><PMID Version="1">18237434</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Biomed Inform. 2010 Dec;43(6):1009-19</RefSource><PMID Version="1">20723615</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2010 Sep 15;26(18):i547-53</RefSource><PMID Version="1">20823320</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Biomed Semantics. 2011;2 Suppl 1:S4</RefSource><PMID Version="1">21388573</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2006 Mar 15;22(6):645-50</RefSource><PMID Version="1">16046493</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Bioinformatics. 2001;17 Suppl 1:S74-82</RefSource><PMID Version="1">11472995</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Pharmacogenomics J. 2001;1(3):167-70</RefSource><PMID Version="1">11908751</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Genome Biol. 2005;6(5):R46</RefSource><PMID Version="1">15892874</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Pharmacogenomics. 2010 Oct;11(10):1467-89</RefSource><PMID Version="1">21047206</PMID></CommentsCorrections></CommentsCorrectionsList><OtherID Source="NLM">PMC3102890</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="aheadofprint"><Year>2011</Year><Month>5</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>6</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2011</Year><Month>6</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2011</Year><Month>6</Month><Day>1</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pii">2041-1480-2-S2-S10</ArticleId><ArticleId IdType="doi">10.1186/2041-1480-2-S2-S10</ArticleId><ArticleId IdType="pubmed">21624156</ArticleId><ArticleId IdType="pmc">PMC3102890</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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