A Novel Metric for Information Retrieval in Semantic Networks
Identifieur interne : 001A29 ( Istex/Corpus ); précédent : 001A28; suivant : 001A30A Novel Metric for Information Retrieval in Semantic Networks
Auteurs : L. Moore ; Florian Steinke ; Volker TrespSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2012.
Abstract
Abstract: We propose a novel graph metric for semantic entity-relationship networks. The metric is used for solving two tasks. First, given a semantic entity-relationship graph, such as for example DBpedia, we find relevant neighbors for a given query node. This could be useful for retrieving information relating to a specific entity. Second, we search for paths between two given nodes to discover interesting links. As an example, this can be helpful to analyze the various relationships between Albert Einstein and Niels Bohr. Compared to using the default step metric our approach yields more specific and informative results, as we demonstrate using two semantic web datasets. The proposed metric is defined via paths that maximize the log-likelihood of a restricted round trip and can intuitively be interpreted in terms of random walks on graphs. Our distance metric is also related to the commute distance, which is highly plausible for the described tasks but prohibitively expensive to compute. Our metric can be calculated efficiently using standard graph algorithms, rendering the approach feasible for the very large graphs of the semantic web’s linked data.
Url:
DOI: 10.1007/978-3-642-25953-1_6
Links to Exploration step
ISTEX:0951B373A3E50E06AB324D71EE453D37AD788D14Le document en format XML
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Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-25953-1</BookID><BookTitle>The Semantic Web: ESWC 2011 Workshops</BookTitle><BookSubTitle>ESWC 2011 Workshops, Heraklion, Greece, May 29-30, 2011, Revised Selected Papers</BookSubTitle><BookVolumeNumber>7117</BookVolumeNumber><BookSequenceNumber>7117</BookSequenceNumber><BookDOI>10.1007/978-3-642-25953-1</BookDOI><BookTitleID>300798</BookTitleID><BookPrintISBN>978-3-642-25952-4</BookPrintISBN><BookElectronicISBN>978-3-642-25953-1</BookElectronicISBN><BookChapterCount>22</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag GmbH Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2012</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I18008" Priority="1" Type="Secondary">Information Systems and Communication Service</BookSubject><BookSubject Code="I21017" Priority="2" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I18040" Priority="3" Type="Secondary">Information Systems Applications (incl. Internet)</BookSubject><BookSubject Code="I13022" Priority="4" Type="Secondary">Computer Communication Networks</BookSubject><BookSubject Code="I18024" Priority="5" Type="Secondary">Database Management</BookSubject><BookSubject Code="I18059" Priority="6" Type="Secondary">Multimedia Information Systems</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>Raúl</GivenName><FamilyName>García-Castro</FamilyName></EditorName><Contact><Email>rgarcia@fi.upm.es</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Dieter</GivenName><FamilyName>Fensel</FamilyName></EditorName><Contact><Email>dieter.fensel@sti2.at</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Grigoris</GivenName><FamilyName>Antoniou</FamilyName></EditorName><Contact><Email>antoniou@ics.forth.gr</Email></Contact></Editor></EditorGroup></BookHeader><Part ID="Part3"><PartInfo TocLevels="0"><PartID>3</PartID><PartSequenceNumber>3</PartSequenceNumber><PartTitle>3rd International Workshop on Inductive Reasoning and Machine Learning for the Semantic Web</PartTitle><PartChapterCount>1</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>The Semantic Web: ESWC 2011 Workshops</BookTitle></PartContext></PartInfo><Chapter ID="Chap6" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>6</ChapterID><ChapterDOI>10.1007/978-3-642-25953-1_6</ChapterDOI><ChapterSequenceNumber>6</ChapterSequenceNumber><ChapterTitle Language="En">A Novel Metric for Information Retrieval in Semantic Networks</ChapterTitle><ChapterFirstPage>65</ChapterFirstPage><ChapterLastPage>79</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2012</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>3</PartID><BookID>978-3-642-25953-1</BookID><BookTitle>The Semantic Web: ESWC 2011 Workshops</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff16 Aff17"><AuthorName DisplayOrder="Western"><GivenName>Joshua</GivenName><GivenName>L.</GivenName><FamilyName>Moore</FamilyName></AuthorName><Contact><Email>jlmo@cs.cornell.edu</Email></Contact></Author><Author AffiliationIDS="Aff16"><AuthorName DisplayOrder="Western"><GivenName>Florian</GivenName><FamilyName>Steinke</FamilyName></AuthorName><Contact><Email>Florian.Steinke@siemens.com</Email></Contact></Author><Author AffiliationIDS="Aff16"><AuthorName DisplayOrder="Western"><GivenName>Volker</GivenName><FamilyName>Tresp</FamilyName></AuthorName><Contact><Email>Volker.Tresp@siemens.com</Email></Contact></Author><Affiliation ID="Aff16"><OrgDivision>Corporate Technology</OrgDivision><OrgName>Siemens AG</OrgName><OrgAddress><City>München</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>We propose a novel graph metric for semantic entity-relationship networks. The metric is used for solving two tasks. First, given a semantic entity-relationship graph, such as for example DBpedia, we find relevant neighbors for a given query node. This could be useful for retrieving information relating to a specific entity. Second, we search for paths between two given nodes to discover interesting links. As an example, this can be helpful to analyze the various relationships between Albert Einstein and Niels Bohr. Compared to using the default step metric our approach yields more specific and informative results, as we demonstrate using two semantic web datasets. The proposed metric is defined via paths that maximize the log-likelihood of a restricted round trip and can intuitively be interpreted in terms of random walks on graphs. Our distance metric is also related to the commute distance, which is highly plausible for the described tasks but prohibitively expensive to compute. Our metric can be calculated efficiently using standard graph algorithms, rendering the approach feasible for the very large graphs of the semantic web’s linked data.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Entity-relationship graph</Keyword><Keyword>information retrieval</Keyword><Keyword>random walk</Keyword><Keyword>commute distance</Keyword><Keyword>graph metric</Keyword><Keyword>path finding</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Novel Metric for Information Retrieval in Semantic Networks</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>A Novel Metric for Information Retrieval in Semantic Networks</title></titleInfo><name type="personal"><namePart type="given">Joshua</namePart><namePart type="given">L.</namePart><namePart type="family">Moore</namePart><affiliation>Corporate Technology, Siemens AG, München, Germany</affiliation><affiliation>Cornell University, Ithaca, NY, USA</affiliation><affiliation>E-mail: jlmo@cs.cornell.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Florian</namePart><namePart type="family">Steinke</namePart><affiliation>Corporate Technology, Siemens AG, München, Germany</affiliation><affiliation>E-mail: Florian.Steinke@siemens.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Volker</namePart><namePart type="family">Tresp</namePart><affiliation>Corporate Technology, Siemens AG, München, Germany</affiliation><affiliation>E-mail: Volker.Tresp@siemens.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [research-article]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2012</dateIssued><copyrightDate encoding="w3cdtf">2012</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: We propose a novel graph metric for semantic entity-relationship networks. The metric is used for solving two tasks. First, given a semantic entity-relationship graph, such as for example DBpedia, we find relevant neighbors for a given query node. This could be useful for retrieving information relating to a specific entity. Second, we search for paths between two given nodes to discover interesting links. As an example, this can be helpful to analyze the various relationships between Albert Einstein and Niels Bohr. Compared to using the default step metric our approach yields more specific and informative results, as we demonstrate using two semantic web datasets. The proposed metric is defined via paths that maximize the log-likelihood of a restricted round trip and can intuitively be interpreted in terms of random walks on graphs. Our distance metric is also related to the commute distance, which is highly plausible for the described tasks but prohibitively expensive to compute. Our metric can be calculated efficiently using standard graph algorithms, rendering the approach feasible for the very large graphs of the semantic web’s linked data.</abstract><relatedItem type="host"><titleInfo><title>The Semantic Web: ESWC 2011 Workshops</title><subTitle>ESWC 2011 Workshops, Heraklion, Greece, May 29-30, 2011, Revised Selected Papers</subTitle></titleInfo><name type="personal"><namePart type="given">Raúl</namePart><namePart type="family">García-Castro</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Dieter</namePart><namePart type="family">Fensel</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Grigoris</namePart><namePart type="family">Antoniou</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2012</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I18008">Information Systems and Communication Service</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I18040">Information Systems Applications (incl. Internet)</topic><topic authority="SpringerSubjectCodes" authorityURI="I13022">Computer Communication Networks</topic><topic authority="SpringerSubjectCodes" authorityURI="I18024">Database Management</topic><topic authority="SpringerSubjectCodes" authorityURI="I18059">Multimedia Information Systems</topic></subject><identifier type="DOI">10.1007/978-3-642-25953-1</identifier><identifier type="ISBN">978-3-642-25952-4</identifier><identifier type="eISBN">978-3-642-25953-1</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">300798</identifier><identifier type="BookID">978-3-642-25953-1</identifier><identifier type="BookChapterCount">22</identifier><identifier type="BookVolumeNumber">7117</identifier><identifier type="BookSequenceNumber">7117</identifier><identifier type="PartChapterCount">1</identifier><part><date>2012</date><detail type="part"><title>3rd International Workshop on Inductive Reasoning and 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Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, Stanford, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>University of California, Los Angeles, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Doug</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbrücken, Germany</affiliation><role><roleTerm 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