Supervised Learning of an Ontology Alignment Process
Identifieur interne : 000F19 ( Istex/Corpus ); précédent : 000F18; suivant : 000F20Supervised Learning of an Ontology Alignment Process
Auteurs : Marc Ehrig ; York Sure ; Steffen StaabSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: Ontology alignment is a crucial task to enable interoperability among different agents. However, the complexity of the alignment task especially for large ontologies requires automated support for the creation of alignment methods. When looking at current ontology alignment methods one can see that they are either not optimized for given ontologies or their optimization by machine learning means is mostly restricted to the extensional definition of ontologies. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches.
Url:
DOI: 10.1007/11590019_58
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However, the complexity of the alignment task especially for large ontologies requires automated support for the creation of alignment methods. When looking at current ontology alignment methods one can see that they are either not optimized for given ontologies or their optimization by machine learning means is mostly restricted to the extensional definition of ontologies. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. 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When looking at current ontology alignment methods one can see that they are either not optimized for given ontologies or their optimization by machine learning means is mostly restricted to the extensional definition of ontologies. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. 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Intelligence</title><editor><persName><forename type="first">David</forename><surname>Hutchison</surname></persName><affiliation>Lancaster University, UK</affiliation></editor><editor><persName><forename type="first">Takeo</forename><surname>Kanade</surname></persName><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation></editor><editor><persName><forename type="first">Josef</forename><surname>Kittler</surname></persName><affiliation>University of Surrey, Guildford, UK</affiliation></editor><editor><persName><forename type="first">Jon</forename><forename type="first">M.</forename><surname>Kleinberg</surname></persName><affiliation>Cornell University, Ithaca, NY, USA</affiliation></editor><editor><persName><forename type="first">Friedemann</forename><surname>Mattern</surname></persName><affiliation>ETH Zurich, Switzerland</affiliation></editor><editor><persName><forename type="first">John</forename><forename type="first">C.</forename><surname>Mitchell</surname></persName><affiliation>Stanford University, CA, USA</affiliation></editor><editor><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation></editor><editor><persName><forename type="first">Oscar</forename><surname>Nierstrasz</surname></persName><affiliation>University of Bern, Switzerland</affiliation></editor><editor><persName><forename type="first">C.</forename><surname>Pandu Rangan</surname></persName><affiliation>Indian Institute of Technology, Madras, India</affiliation></editor><editor><persName><forename type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation>University of Dortmund, Germany</affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation></editor><editor><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation>New York University, NY, USA</affiliation></editor><editor><persName><forename type="first">Dough</forename><surname>Tygar</surname></persName><affiliation>University of California, Berkeley, CA, USA</affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation>Rice University, Houston, TX, USA</affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation>Max-Planck Institute of Computer Science, Saarbruecken, Germany</affiliation></editor><editor><persName><forename type="first">Jaime</forename><forename type="first">G.</forename><surname>Carbonell</surname></persName><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation></editor><editor><persName><forename type="first">Jörg</forename><surname>Siekmann</surname></persName><affiliation>University of Saarland, Saarbrücken, Germany</affiliation></editor><editor><persName><forename type="first">Klaus-Dieter</forename><surname>Althoff</surname></persName><email>althoff@iis.uni-hildesheim.de</email><affiliation>Institute of Computer Science, University of Hildesheim, Marienburger Platz 22, 31141, Hildesheim, Germany</affiliation></editor><editor><persName><forename type="first">Andreas</forename><surname>Dengel</surname></persName><email>Andreas.Dengel@dfki.de</email><affiliation>Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany</affiliation></editor><editor><persName><forename type="first">Ralph</forename><surname>Bergmann</surname></persName><email>bergmann@uni-trier.de</email><affiliation>Department of Business Information Systems II, University of Trier, Trier, Germany</affiliation></editor><editor><persName><forename type="first">Markus</forename><surname>Nick</surname></persName><email>nick@iese.fraunhofer.de</email><affiliation>Department for Experience Management, Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern</affiliation></editor><editor><persName><forename type="first">Thomas</forename><surname>Roth-Berghofer</surname></persName><email>Thomas.Roth-Berghofer@dfki.de</email><affiliation>Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Trippstadter Straße 122, 67663, Kaiserslautern, Germany</affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><biblScope unit="seriesId">1244</biblScope></series><idno type="istex">BEE11535AC97B0B47EF401CFB0CDC5FEFAA93E18</idno><idno type="DOI">10.1007/11590019_58</idno><idno type="ChapterID">58</idno><idno type="ChapterID">Chap58</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2005</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: Ontology alignment is a crucial task to enable interoperability among different agents. However, the complexity of the alignment task especially for large ontologies requires automated support for the creation of alignment methods. When looking at current ontology alignment methods one can see that they are either not optimized for given ontologies or their optimization by machine learning means is mostly restricted to the extensional definition of ontologies. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches.</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><label>I21017</label><label>I18032</label><label>I18040</label><label>I2301X</label><label>I24032</label><item><term>Computer Science</term></item><item><term>Artificial Intelligence (incl. Robotics)</term></item><item><term>Information Storage and Retrieval</term></item><item><term>Information Systems Applications (incl.Internet)</term></item><item><term>Computer Appl. in Administrative Data Processing</term></item><item><term>Computers and Education</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2005">Published</change><change xml:id="refBibs-istex" who="#ISTEX-API" when="2016-11-22">References added</change><change xml:id="refBibs-istex" who="#ISTEX-API" when="2017-01-20">References added</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/BEE11535AC97B0B47EF401CFB0CDC5FEFAA93E18/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Springer, Publisher found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType 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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>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesPrintISSN>0302-9743</SubSeriesPrintISSN><SubSeriesElectronicISSN>1611-3349</SubSeriesElectronicISSN><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle></SubSeriesInfo><SubSeriesHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Jaime</GivenName><GivenName>G.</GivenName><FamilyName>Carbonell</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Affiliation 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22</Street><Postcode>31141</Postcode><City>Hildesheim</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgDivision>Knowledge Management Department</OrgDivision><OrgName>German Research Center for Artificial Intelligence (DFKI) GmbH</OrgName><OrgAddress><City>Kaiserslautern</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>Department of Business Information Systems II</OrgDivision><OrgName>University of Trier</OrgName><OrgAddress><City>Trier</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff21"><OrgDivision>Department for Experience Management</OrgDivision><OrgName>Fraunhofer Institute for Experimental Software Engineering</OrgName><OrgAddress><City>Kaiserslautern</City></OrgAddress></Affiliation><Affiliation ID="Aff22"><OrgDivision>Knowledge Management Department</OrgDivision><OrgName>German Research Center for Artificial Intelligence (DFKI) GmbH</OrgName><OrgAddress><Street>Trippstadter Straße 122</Street><Postcode>67663</Postcode><City>Kaiserslautern</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part12"><PartInfo TocLevels="0"><PartID>12</PartID><PartSequenceNumber>12</PartSequenceNumber><PartTitle>Intelligent IT Tools for Knowledge Management Systems: (KMTOOLS 2005)</PartTitle><PartChapterCount>6</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Professional Knowledge Management</BookTitle></PartContext></PartInfo><Chapter ID="Chap58" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>58</ChapterID><ChapterDOI>10.1007/11590019_58</ChapterDOI><ChapterSequenceNumber>58</ChapterSequenceNumber><ChapterTitle Language="En">Supervised Learning of an Ontology Alignment Process</ChapterTitle><ChapterFirstPage>508</ChapterFirstPage><ChapterLastPage>517</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</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>12</PartID><BookID>978-3-540-31620-6</BookID><BookTitle>Professional Knowledge Management</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff23"><AuthorName DisplayOrder="Western"><GivenName>Marc</GivenName><FamilyName>Ehrig</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff23"><AuthorName DisplayOrder="Western"><GivenName>York</GivenName><FamilyName>Sure</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff24"><AuthorName DisplayOrder="Western"><GivenName>Steffen</GivenName><FamilyName>Staab</FamilyName></AuthorName></Author><Affiliation ID="Aff23"><OrgDivision>Institute AIFB</OrgDivision><OrgName>University of Karlsruhe</OrgName><OrgAddress><Country> </Country></OrgAddress></Affiliation><Affiliation ID="Aff24"><OrgDivision>ISWeb</OrgDivision><OrgName>University of Koblenz-Landau</OrgName><OrgAddress><Country> </Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Ontology alignment is a crucial task to enable interoperability among different agents. However, the complexity of the alignment task especially for large ontologies requires automated support for the creation of alignment methods. When looking at current ontology alignment methods one can see that they are either not optimized for given ontologies or their optimization by machine learning means is mostly restricted to the extensional definition of ontologies. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Supervised Learning of an Ontology Alignment Process</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Supervised Learning of an Ontology Alignment Process</title></titleInfo><name type="personal"><namePart type="given">Marc</namePart><namePart type="family">Ehrig</namePart><affiliation>Institute AIFB, University of Karlsruhe</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">York</namePart><namePart type="family">Sure</namePart><affiliation>Institute AIFB, University of Karlsruhe</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Steffen</namePart><namePart type="family">Staab</namePart><affiliation>ISWeb, University of Koblenz-Landau</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</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: Ontology alignment is a crucial task to enable interoperability among different agents. However, the complexity of the alignment task especially for large ontologies requires automated support for the creation of alignment methods. When looking at current ontology alignment methods one can see that they are either not optimized for given ontologies or their optimization by machine learning means is mostly restricted to the extensional definition of ontologies. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches.</abstract><relatedItem type="host"><titleInfo><title>Professional Knowledge Management</title><subTitle>Third Biennial Conference, WM 2005, Kaiserslautern, Germany, April 10-13, 2005, Revised Selected Papers</subTitle></titleInfo><name type="personal"><namePart type="given">Klaus-Dieter</namePart><namePart type="family">Althoff</namePart><affiliation>Institute of Computer Science, University of Hildesheim, Marienburger Platz 22, 31141, Hildesheim, Germany</affiliation><affiliation>E-mail: althoff@iis.uni-hildesheim.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Andreas</namePart><namePart type="family">Dengel</namePart><affiliation>Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany</affiliation><affiliation>E-mail: Andreas.Dengel@dfki.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ralph</namePart><namePart type="family">Bergmann</namePart><affiliation>Department of Business Information Systems II, University of Trier, Trier, Germany</affiliation><affiliation>E-mail: bergmann@uni-trier.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Markus</namePart><namePart type="family">Nick</namePart><affiliation>Department for Experience Management, Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern</affiliation><affiliation>E-mail: nick@iese.fraunhofer.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Thomas</namePart><namePart type="family">Roth-Berghofer</namePart><affiliation>Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Trippstadter Straße 122, 67663, Kaiserslautern, Germany</affiliation><affiliation>E-mail: Thomas.Roth-Berghofer@dfki.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2005</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="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I18032">Information Storage and Retrieval</topic><topic authority="SpringerSubjectCodes" authorityURI="I18040">Information Systems Applications (incl.Internet)</topic><topic authority="SpringerSubjectCodes" authorityURI="I2301X">Computer Appl. in Administrative Data Processing</topic><topic authority="SpringerSubjectCodes" authorityURI="I24032">Computers and Education</topic></subject><identifier type="DOI">10.1007/11590019</identifier><identifier type="ISBN">978-3-540-30465-4</identifier><identifier type="eISBN">978-3-540-31620-6</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">127867</identifier><identifier type="BookID">978-3-540-31620-6</identifier><identifier type="BookChapterCount">82</identifier><identifier type="BookVolumeNumber">3782</identifier><identifier type="BookSequenceNumber">3782</identifier><identifier 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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</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, 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, 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, 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, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart 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Institute of Computer Science, Saarbruecken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2005</copyrightDate><issuance>serial</issuance></originInfo><relatedItem type="constituent"><titleInfo><title>Lecture Notes in Artificial Intelligence</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, 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</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, 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, 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, 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, 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>New York University, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Dough</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, Saarbruecken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jaime</namePart><namePart type="given">G.</namePart><namePart type="family">Carbonell</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jörg</namePart><namePart type="family">Siekmann</namePart><affiliation>University of Saarland, Saarbrücken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Klaus-Dieter</namePart><namePart type="family">Althoff</namePart><affiliation>Institute of Computer Science, University of Hildesheim, Marienburger Platz 22, 31141, Hildesheim, Germany</affiliation><affiliation>E-mail: althoff@iis.uni-hildesheim.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Andreas</namePart><namePart type="family">Dengel</namePart><affiliation>Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany</affiliation><affiliation>E-mail: Andreas.Dengel@dfki.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Ralph</namePart><namePart type="family">Bergmann</namePart><affiliation>Department of Business Information Systems II, University of Trier, Trier, Germany</affiliation><affiliation>E-mail: bergmann@uni-trier.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Markus</namePart><namePart type="family">Nick</namePart><affiliation>Department for Experience Management, Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern</affiliation><affiliation>E-mail: nick@iese.fraunhofer.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Thomas</namePart><namePart type="family">Roth-Berghofer</namePart><affiliation>Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Trippstadter Straße 122, 67663, Kaiserslautern, Germany</affiliation><affiliation>E-mail: Thomas.Roth-Berghofer@dfki.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="sub-series"></genre><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SubSeriesID">1244</identifier></relatedItem><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></relatedItem><identifier type="istex">BEE11535AC97B0B47EF401CFB0CDC5FEFAA93E18</identifier><identifier type="DOI">10.1007/11590019_58</identifier><identifier type="ChapterID">58</identifier><identifier type="ChapterID">Chap58</identifier><accessCondition type="use and reproduction" 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