Topic Difficulty Prediction in Entity Ranking
Identifieur interne : 000453 ( Main/Corpus ); précédent : 000452; suivant : 000454Topic Difficulty Prediction in Entity Ranking
Auteurs : Anne-Marie Vercoustre ; Jovan Pehcevski ; Vladimir NaumovskiSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
Abstract
Abstract: Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking.
Url:
DOI: 10.1007/978-3-642-03761-0_29
Links to Exploration step
ISTEX:C86A4C817A8FB818CB2B7E9CAE24ADCD8CDC04ECLe document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Topic Difficulty Prediction in Entity Ranking</title><author><name sortKey="Vercoustre, Anne Marie" sort="Vercoustre, Anne Marie" uniqKey="Vercoustre A" first="Anne-Marie" last="Vercoustre">Anne-Marie Vercoustre</name><affiliation><mods:affiliation>INRIA, Rocquencourt, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: anne-marie.vercoustre@inria.fr</mods:affiliation></affiliation></author><author><name sortKey="Pehcevski, Jovan" sort="Pehcevski, Jovan" uniqKey="Pehcevski J" first="Jovan" last="Pehcevski">Jovan Pehcevski</name><affiliation><mods:affiliation>Faculty of Management and Information Technologies, Skopje, Macedonia</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: jovan.pehcevski@mit.edu.mk</mods:affiliation></affiliation></author><author><name sortKey="Naumovski, Vladimir" sort="Naumovski, Vladimir" uniqKey="Naumovski V" first="Vladimir" last="Naumovski">Vladimir Naumovski</name><affiliation><mods:affiliation>Faculty of Management and Information Technologies, Skopje, Macedonia</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: vladimir.naumovski@mit.edu.mk</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:C86A4C817A8FB818CB2B7E9CAE24ADCD8CDC04EC</idno><date when="2009" year="2009">2009</date><idno type="doi">10.1007/978-3-642-03761-0_29</idno><idno type="url">https://api.istex.fr/document/C86A4C817A8FB818CB2B7E9CAE24ADCD8CDC04EC/fulltext/pdf</idno><idno type="wicri:Area/Main/Corpus">000453</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="ISTEX">000453</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Topic Difficulty Prediction in Entity Ranking</title><author><name sortKey="Vercoustre, Anne Marie" sort="Vercoustre, Anne Marie" uniqKey="Vercoustre A" first="Anne-Marie" last="Vercoustre">Anne-Marie Vercoustre</name><affiliation><mods:affiliation>INRIA, Rocquencourt, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: anne-marie.vercoustre@inria.fr</mods:affiliation></affiliation></author><author><name sortKey="Pehcevski, Jovan" sort="Pehcevski, Jovan" uniqKey="Pehcevski J" first="Jovan" last="Pehcevski">Jovan Pehcevski</name><affiliation><mods:affiliation>Faculty of Management and Information Technologies, Skopje, Macedonia</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: jovan.pehcevski@mit.edu.mk</mods:affiliation></affiliation></author><author><name sortKey="Naumovski, Vladimir" sort="Naumovski, Vladimir" uniqKey="Naumovski V" first="Vladimir" last="Naumovski">Vladimir Naumovski</name><affiliation><mods:affiliation>Faculty of Management and Information Technologies, Skopje, Macedonia</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: vladimir.naumovski@mit.edu.mk</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2009</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">C86A4C817A8FB818CB2B7E9CAE24ADCD8CDC04EC</idno><idno type="DOI">10.1007/978-3-642-03761-0_29</idno><idno type="ChapterID">Chap29</idno><idno type="ChapterID">29</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Anne-Marie Vercoustre</name><affiliations><json:string>INRIA, Rocquencourt, France</json:string><json:string>E-mail: anne-marie.vercoustre@inria.fr</json:string></affiliations></json:item><json:item><name>Jovan Pehcevski</name><affiliations><json:string>Faculty of Management and Information Technologies, Skopje, Macedonia</json:string><json:string>E-mail: jovan.pehcevski@mit.edu.mk</json:string></affiliations></json:item><json:item><name>Vladimir Naumovski</name><affiliations><json:string>Faculty of Management and Information Technologies, Skopje, Macedonia</json:string><json:string>E-mail: vladimir.naumovski@mit.edu.mk</json:string></affiliations></json:item></author><genre><json:string>Original Paper</json:string></genre><serie><editor><json:item><name>David Hutchison</name><affiliations><json:string>Lancaster University, Lancaster, UK</json:string></affiliations></json:item><json:item><name>Takeo Kanade</name><affiliations><json:string>Carnegie Mellon University, Pittsburgh, PA, USA</json:string></affiliations></json:item><json:item><name>Josef Kittler</name><affiliations><json:string>University of Surrey, Guildford, UK</json:string></affiliations></json:item><json:item><name>Jon M. 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Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. 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Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><item><term>Computer Science</term></item><label>I18030</label><item><term>Data Mining and Knowledge Discovery</term></item><label>I18032</label><item><term>Information Storage and Retrieval</term></item><label>I18024</label><item><term>Database Management</term></item><label>I18040</label><item><term>Information Systems Applications (incl.Internet)</term></item><label>I15025</label><item><term>Data Storage Representation</term></item><label>I15017</label><item><term>Data 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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" NumberingStyle="Unnumbered" OutputMedium="All" TocLevels="0"><BookID>978-3-642-03761-0</BookID><BookTitle>Advances in Focused Retrieval</BookTitle><BookSubTitle>7th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2008, Dagstuhl Castle, Germany, December 15-18, 2008. Revised and Selected Papers</BookSubTitle><BookVolumeNumber>5631</BookVolumeNumber><BookSequenceNumber>5631</BookSequenceNumber><BookDOI>10.1007/978-3-642-03761-0</BookDOI><BookTitleID>190418</BookTitleID><BookPrintISBN>978-3-642-03760-3</BookPrintISBN><BookElectronicISBN>978-3-642-03761-0</BookElectronicISBN><BookChapterCount>49</BookChapterCount><BookCopyright><CopyrightHolderName>Springer Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2009</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I18030" Priority="1" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><BookSubject Code="I18032" Priority="2" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="I18024" Priority="3" Type="Secondary">Database Management</BookSubject><BookSubject Code="I18040" Priority="4" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I15025" Priority="5" Type="Secondary">Data Storage Representation</BookSubject><BookSubject Code="I15017" Priority="6" Type="Secondary">Data Structures</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Shlomo</GivenName><FamilyName>Geva</FamilyName></EditorName><Contact><Email>s.geva@qut.edu.au</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Jaap</GivenName><FamilyName>Kamps</FamilyName></EditorName><Contact><Email>kamps@uva.nl</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Andrew</GivenName><FamilyName>Trotman</FamilyName></EditorName><Contact><Email>andrew@cs.otago.ac.nz</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Faculty of Science and Technology</OrgDivision><OrgName>Queensland University of Technology</OrgName><OrgAddress><Postbox>GPO Box 2434</Postbox><Postcode>4001</Postcode><City>Brisband</City><State>Qld</State><Country>Australia</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Archives and Information Studies/Humanities</OrgDivision><OrgName>University of Amsterdam</OrgName><OrgAddress><Street>Turfdraagsterpad 9</Street><Postcode>1012 XT</Postcode><City>Amsterdam</City><Country>The Netherlands</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Department of Computer Science</OrgDivision><OrgName>University of Otago</OrgName><OrgAddress><Postbox>P.O. Box 56</Postbox><Postcode>9054</Postcode><City>Dunedin</City><Country>New Zealand</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part4"><PartInfo TocLevels="0"><PartID>4</PartID><PartSequenceNumber>4</PartSequenceNumber><PartTitle>Entity Ranking Track</PartTitle><PartChapterCount>6</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Advances in Focused Retrieval</BookTitle></PartContext></PartInfo><Chapter ID="Chap29" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>29</ChapterID><ChapterDOI>10.1007/978-3-642-03761-0_29</ChapterDOI><ChapterSequenceNumber>29</ChapterSequenceNumber><ChapterTitle Language="En">Topic Difficulty Prediction in Entity Ranking</ChapterTitle><ChapterFirstPage>280</ChapterFirstPage><ChapterLastPage>291</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2009</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>4</PartID><BookID>978-3-642-03761-0</BookID><BookTitle>Advances in Focused Retrieval</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Anne-Marie</GivenName><FamilyName>Vercoustre</FamilyName></AuthorName><Contact><Email>anne-marie.vercoustre@inria.fr</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Jovan</GivenName><FamilyName>Pehcevski</FamilyName></AuthorName><Contact><Email>jovan.pehcevski@mit.edu.mk</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Vladimir</GivenName><FamilyName>Naumovski</FamilyName></AuthorName><Contact><Email>vladimir.naumovski@mit.edu.mk</Email></Contact></Author><Affiliation ID="Aff19"><OrgName>INRIA, Rocquencourt</OrgName><OrgAddress><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgName>Faculty of Management and Information Technologies</OrgName><OrgAddress><City>Skopje</City><Country>Macedonia</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted <Emphasis Type="Italic">classes</Emphasis> of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><!--Version 0.11 générée le 11-8-2015--><mods version="3.6"><titleInfo lang="en"><title>Topic Difficulty Prediction in Entity Ranking</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Topic Difficulty Prediction in Entity Ranking</title></titleInfo><name type="personal"><namePart type="given">Anne-Marie</namePart><namePart type="family">Vercoustre</namePart><affiliation>INRIA, Rocquencourt, France</affiliation><affiliation>E-mail: anne-marie.vercoustre@inria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jovan</namePart><namePart type="family">Pehcevski</namePart><affiliation>Faculty of Management and Information Technologies, Skopje, Macedonia</affiliation><affiliation>E-mail: jovan.pehcevski@mit.edu.mk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Vladimir</namePart><namePart type="family">Naumovski</namePart><affiliation>Faculty of Management and Information Technologies, Skopje, Macedonia</affiliation><affiliation>E-mail: vladimir.naumovski@mit.edu.mk</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre>Original Paper</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><copyrightDate encoding="w3cdtf">2009</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: Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking.</abstract><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, Lancaster, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon 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 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 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 type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2009</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer Berlin Heidelberg, 2009</recordOrigin></recordInfo></relatedItem><relatedItem type="host"><titleInfo><title>Advances in Focused Retrieval</title><subTitle>7th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2008, Dagstuhl Castle, Germany, December 15-18, 2008. Revised and Selected Papers</subTitle></titleInfo><name type="personal"><namePart type="given">Shlomo</namePart><namePart type="family">Geva</namePart><affiliation>Faculty of Science and Technology, Queensland University of Technology, GPO Box 2434, 4001, Brisband, Qld, Australia</affiliation><affiliation>E-mail: s.geva@qut.edu.au</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jaap</namePart><namePart type="family">Kamps</namePart><affiliation>Archives and Information Studies/Humanities, University of Amsterdam, Turfdraagsterpad 9, 1012 XT, Amsterdam, The Netherlands</affiliation><affiliation>E-mail: kamps@uva.nl</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Andrew</namePart><namePart type="family">Trotman</namePart><affiliation>Department of Computer Science, University of Otago, P.O. Box 56, 9054, Dunedin, New Zealand</affiliation><affiliation>E-mail: andrew@cs.otago.ac.nz</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre>Proceedings</genre><genre>eBook</genre><originInfo><copyrightDate encoding="w3cdtf">2009</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="I18030">Data Mining and Knowledge Discovery</topic><topic authority="SpringerSubjectCodes" authorityURI="I18032">Information Storage and Retrieval</topic><topic authority="SpringerSubjectCodes" authorityURI="I18024">Database Management</topic><topic authority="SpringerSubjectCodes" authorityURI="I18040">Information Systems Applications (incl.Internet)</topic><topic authority="SpringerSubjectCodes" authorityURI="I15025">Data Storage Representation</topic><topic authority="SpringerSubjectCodes" authorityURI="I15017">Data Structures</topic></subject><identifier type="DOI">10.1007/978-3-642-03761-0</identifier><identifier type="ISBN">978-3-642-03760-3</identifier><identifier type="eISBN">978-3-642-03761-0</identifier><identifier type="BookTitleID">190418</identifier><identifier type="BookID">978-3-642-03761-0</identifier><identifier type="BookChapterCount">49</identifier><identifier type="BookVolumeNumber">5631</identifier><identifier type="BookSequenceNumber">5631</identifier><identifier type="PartChapterCount">6</identifier><part><date>2009</date><detail type="part"><title>Entity Ranking Track</title></detail><detail type="volume"><number>5631</number><caption>vol.</caption></detail><extent unit="pages"><start>280</start><end>291</end></extent></part><recordInfo><recordOrigin>Springer Berlin Heidelberg, 2009</recordOrigin></recordInfo></relatedItem><identifier type="istex">C86A4C817A8FB818CB2B7E9CAE24ADCD8CDC04EC</identifier><identifier type="DOI">10.1007/978-3-642-03761-0_29</identifier><identifier type="ChapterID">Chap29</identifier><identifier type="ChapterID">29</identifier><accessCondition type="use and reproduction" contentType="MetadataGrant">OpenAccess</accessCondition><accessCondition type="use and reproduction" contentType="AbstractGrant">OpenAccess</accessCondition><accessCondition type="use and reproduction" contentType="BodyPDFGrant">Restricted</accessCondition><accessCondition type="use and reproduction" contentType="BodyHTMLGrant">Restricted</accessCondition><accessCondition type="use and reproduction" contentType="BibliographyGrant">Restricted</accessCondition><accessCondition type="use and reproduction" contentType="ESMGrant">Restricted</accessCondition><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2009</recordOrigin></recordInfo></mods></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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