Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach
Identifieur interne : 001660 ( Istex/Corpus ); précédent : 001659; suivant : 001661Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach
Auteurs : Xavier Polanco ; Claire François ; Jean-Charles LamirelSource :
- Scientometrics [ 0138-9130 ] ; 2001-04-01.
Abstract
Abstract: We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.
Url:
DOI: 10.1023/A:1010537316758
Links to Exploration step
ISTEX:611BC9E52B694ADA5748DD91EEF5379215628568Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach</title><author><name sortKey="Polanco, Xavier" sort="Polanco, Xavier" uniqKey="Polanco X" first="Xavier" last="Polanco">Xavier Polanco</name><affiliation><mods:affiliation>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: polanco@inist.fr</mods:affiliation></affiliation></author><author><name sortKey="Francois, Claire" sort="Francois, Claire" uniqKey="Francois C" first="Claire" last="François">Claire François</name><affiliation><mods:affiliation>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</mods:affiliation></affiliation></author><author><name sortKey="Lamirel, Jean Charles" sort="Lamirel, Jean Charles" uniqKey="Lamirel J" first="Jean-Charles" last="Lamirel">Jean-Charles Lamirel</name><affiliation><mods:affiliation>LORIA, Vandoeuvre-lès-Nancy, (France)</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:611BC9E52B694ADA5748DD91EEF5379215628568</idno><date when="2001" year="2001">2001</date><idno type="doi">10.1023/A:1010537316758</idno><idno type="url">https://api.istex.fr/ark:/67375/VQC-RCF0SGC7-H/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">001660</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001660</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach</title><author><name sortKey="Polanco, Xavier" sort="Polanco, Xavier" uniqKey="Polanco X" first="Xavier" last="Polanco">Xavier Polanco</name><affiliation><mods:affiliation>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: polanco@inist.fr</mods:affiliation></affiliation></author><author><name sortKey="Francois, Claire" sort="Francois, Claire" uniqKey="Francois C" first="Claire" last="François">Claire François</name><affiliation><mods:affiliation>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</mods:affiliation></affiliation></author><author><name sortKey="Lamirel, Jean Charles" sort="Lamirel, Jean Charles" uniqKey="Lamirel J" first="Jean-Charles" last="Lamirel">Jean-Charles Lamirel</name><affiliation><mods:affiliation>LORIA, Vandoeuvre-lès-Nancy, (France)</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Scientometrics</title><title level="j" type="sub">An International Journal for all Quantitative Aspects of the Science of Science, Communication in Science and Science Policy</title><title level="j" type="abbrev">Scientometrics</title><idno type="ISSN">0138-9130</idno><idno type="eISSN">1588-2861</idno><imprint><publisher>Kluwer Academic Publishers</publisher><pubPlace>Dordrecht</pubPlace><date type="published" when="2001-04-01">2001-04-01</date><biblScope unit="volume">51</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="267">267</biblScope><biblScope unit="page" to="292">292</biblScope></imprint><idno type="ISSN">0138-9130</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0138-9130</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.</div></front></TEI><istex><corpusName>springer-journals</corpusName><author><json:item><name>Xavier Polanco</name><affiliations><json:string>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</json:string><json:string>E-mail: polanco@inist.fr</json:string></affiliations></json:item><json:item><name>Claire François</name><affiliations><json:string>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</json:string></affiliations></json:item><json:item><name>Jean-Charles Lamirel</name><affiliations><json:string>LORIA, Vandoeuvre-lès-Nancy, (France)</json:string></affiliations></json:item></author><articleId><json:string>359773</json:string><json:string>Art14</json:string></articleId><arkIstex>ark:/67375/VQC-RCF0SGC7-H</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.</abstract><qualityIndicators><refBibsNative>false</refBibsNative><abstractWordCount>225</abstractWordCount><abstractCharCount>1628</abstractCharCount><keywordCount>0</keywordCount><score>9.7</score><pdfWordCount>8878</pdfWordCount><pdfCharCount>52398</pdfCharCount><pdfVersion>1.3</pdfVersion><pdfPageCount>26</pdfPageCount><pdfPageSize>595 x 842 pts (A4)</pdfPageSize></qualityIndicators><title>Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach</title><genre><json:string>research-article</json:string></genre><host><title>Scientometrics</title><language><json:string>unknown</json:string></language><publicationDate>2001</publicationDate><copyrightDate>2001</copyrightDate><issn><json:string>0138-9130</json:string></issn><eissn><json:string>1588-2861</json:string></eissn><journalId><json:string>11192</json:string></journalId><volume>51</volume><issue>1</issue><pages><first>267</first><last>292</last></pages><genre><json:string>journal</json:string></genre><subject><json:item><value>Information Storage and Retrieval</value></json:item><json:item><value>Interdisciplinary Studies</value></json:item><json:item><value>Library Science</value></json:item></subject></host><ark><json:string>ark:/67375/VQC-RCF0SGC7-H</json:string></ark><publicationDate>2001</publicationDate><copyrightDate>2001</copyrightDate><doi><json:string>10.1023/A:1010537316758</json:string></doi><id>611BC9E52B694ADA5748DD91EEF5379215628568</id><score>1</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/ark:/67375/VQC-RCF0SGC7-H/fulltext.pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/ark:/67375/VQC-RCF0SGC7-H/bundle.zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/ark:/67375/VQC-RCF0SGC7-H/fulltext.tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher scheme="https://scientific-publisher.data.istex.fr">Kluwer Academic Publishers</publisher><pubPlace>Dordrecht</pubPlace><availability><licence><p>Kluwer Academic Publishers/Akadémiai Kiadó, 2001</p></licence><p scheme="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-3XSW68JL-F">springer</p></availability><date>2001</date></publicationStmt><notesStmt><note type="research-article" scheme="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</note><note type="journal" scheme="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach</title><author xml:id="author-0000"><persName><forename type="first">Xavier</forename><surname>Polanco</surname></persName><email>polanco@inist.fr</email><affiliation>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</affiliation></author><author xml:id="author-0001"><persName><forename type="first">Claire</forename><surname>François</surname></persName><affiliation>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</affiliation></author><author xml:id="author-0002"><persName><forename type="first">Jean-Charles</forename><surname>Lamirel</surname></persName><affiliation>LORIA, Vandoeuvre-lès-Nancy, (France)</affiliation></author><idno type="istex">611BC9E52B694ADA5748DD91EEF5379215628568</idno><idno type="ark">ark:/67375/VQC-RCF0SGC7-H</idno><idno type="DOI">10.1023/A:1010537316758</idno><idno type="article-id">359773</idno><idno type="article-id">Art14</idno></analytic><monogr><title level="j">Scientometrics</title><title level="j" type="sub">An International Journal for all Quantitative Aspects of the Science of Science, Communication in Science and Science Policy</title><title level="j" type="abbrev">Scientometrics</title><idno type="pISSN">0138-9130</idno><idno type="eISSN">1588-2861</idno><idno type="journal-ID">true</idno><idno type="issue-article-count">19</idno><idno type="volume-issue-count">3</idno><imprint><publisher>Kluwer Academic Publishers</publisher><pubPlace>Dordrecht</pubPlace><date type="published" when="2001-04-01"></date><biblScope unit="volume">51</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="267">267</biblScope><biblScope unit="page" to="292">292</biblScope></imprint></monogr></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2001</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.</p></abstract><textClass><keywords scheme="Journal Subject"><list><head>Computer Science</head><item><term>Information Storage and Retrieval</term></item><item><term>Interdisciplinary Studies</term></item><item><term>Library Science</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2001-04-01">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/VQC-RCF0SGC7-H/fulltext.txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus springer-journals not found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//Springer-Verlag//DTD A++ V2.4//EN" URI="http://devel.springer.de/A++/V2.4/DTD/A++V2.4.dtd" name="istex:docType"></istex:docType><istex:document><Publisher><PublisherInfo><PublisherName>Kluwer Academic Publishers</PublisherName><PublisherLocation>Dordrecht</PublisherLocation></PublisherInfo><Journal><JournalInfo JournalProductType="ArchiveJournal" NumberingStyle="Unnumbered"><JournalID>11192</JournalID><JournalPrintISSN>0138-9130</JournalPrintISSN><JournalElectronicISSN>1588-2861</JournalElectronicISSN><JournalTitle>Scientometrics</JournalTitle><JournalSubTitle>An International Journal for all Quantitative Aspects of the Science of Science, Communication in Science and Science Policy</JournalSubTitle><JournalAbbreviatedTitle>Scientometrics</JournalAbbreviatedTitle><JournalSubjectGroup><JournalSubject Type="Primary">Computer Science</JournalSubject><JournalSubject Type="Secondary">Information Storage and Retrieval</JournalSubject><JournalSubject Type="Secondary">Interdisciplinary Studies</JournalSubject><JournalSubject Type="Secondary">Library Science</JournalSubject></JournalSubjectGroup></JournalInfo><Volume><VolumeInfo VolumeType="Regular" TocLevels="0"><VolumeIDStart>51</VolumeIDStart><VolumeIDEnd>51</VolumeIDEnd><VolumeIssueCount>3</VolumeIssueCount></VolumeInfo><Issue IssueType="Regular"><IssueInfo TocLevels="0"><IssueIDStart>1</IssueIDStart><IssueIDEnd>1</IssueIDEnd><IssueArticleCount>19</IssueArticleCount><IssueHistory><CoverDate><Year>2001</Year><Month>4</Month></CoverDate></IssueHistory><IssueCopyright><CopyrightHolderName>Kluwer Academic Publishers/Akadémiai Kiadó</CopyrightHolderName><CopyrightYear>2001</CopyrightYear></IssueCopyright></IssueInfo><Article ID="Art14"><ArticleInfo Language="En" ArticleType="OriginalPaper" NumberingStyle="Unnumbered" TocLevels="0" ContainsESM="No"><ArticleID>359773</ArticleID><ArticleDOI>10.1023/A:1010537316758</ArticleDOI><ArticleSequenceNumber>14</ArticleSequenceNumber><ArticleTitle Language="En">Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach</ArticleTitle><ArticleFirstPage>267</ArticleFirstPage><ArticleLastPage>292</ArticleLastPage><ArticleHistory><RegistrationDate><Year>2004</Year><Month>10</Month><Day>16</Day></RegistrationDate></ArticleHistory><ArticleCopyright><CopyrightHolderName>Kluwer Academic Publishers/Akadémiai Kiadó</CopyrightHolderName><CopyrightYear>2001</CopyrightYear></ArticleCopyright><ArticleGrants 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></ArticleGrants><ArticleContext><JournalID>11192</JournalID><VolumeIDStart>51</VolumeIDStart><VolumeIDEnd>51</VolumeIDEnd><IssueIDStart>1</IssueIDStart><IssueIDEnd>1</IssueIDEnd></ArticleContext></ArticleInfo><ArticleHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Xavier</GivenName><FamilyName>Polanco</FamilyName></AuthorName><Contact><Email>polanco@inist.fr</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Claire</GivenName><FamilyName>François</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Jean-Charles</GivenName><FamilyName>Lamirel</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgName>URI/INIST-CNRS</OrgName><OrgAddress><City>Vandoeuvre-lès-Nancy</City><Country>(France)</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>LORIA</OrgName><OrgAddress><City>Vandoeuvre-lès-Nancy</City><Country>(France)</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.</Para></Abstract></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach</title></titleInfo><name type="personal"><namePart type="given">Xavier</namePart><namePart type="family">Polanco</namePart><affiliation>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</affiliation><affiliation>E-mail: polanco@inist.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Claire</namePart><namePart type="family">François</namePart><affiliation>URI/INIST-CNRS, Vandoeuvre-lès-Nancy, (France)</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jean-Charles</namePart><namePart type="family">Lamirel</namePart><affiliation>LORIA, Vandoeuvre-lès-Nancy, (France)</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>Kluwer Academic Publishers</publisher><place><placeTerm type="text">Dordrecht</placeTerm></place><dateIssued encoding="w3cdtf">2001-04-01</dateIssued><copyrightDate encoding="w3cdtf">2001</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.</abstract><relatedItem type="host"><titleInfo><title>Scientometrics</title><subTitle>An International Journal for all Quantitative Aspects of the Science of Science, Communication in Science and Science Policy</subTitle></titleInfo><titleInfo type="abbreviated"><title>Scientometrics</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><originInfo><publisher>Springer</publisher><dateIssued encoding="w3cdtf">2001-04-01</dateIssued><copyrightDate encoding="w3cdtf">2001</copyrightDate></originInfo><subject><genre>Computer Science</genre><topic>Information Storage and Retrieval</topic><topic>Interdisciplinary Studies</topic><topic>Library Science</topic></subject><identifier type="ISSN">0138-9130</identifier><identifier type="eISSN">1588-2861</identifier><identifier type="JournalID">11192</identifier><identifier type="IssueArticleCount">19</identifier><identifier type="VolumeIssueCount">3</identifier><part><date>2001</date><detail type="volume"><number>51</number><caption>vol.</caption></detail><detail type="issue"><number>1</number><caption>no.</caption></detail><extent unit="pages"><start>267</start><end>292</end></extent></part><recordInfo><recordOrigin>Kluwer Academic Publishers/Akadémiai Kiadó, 2001</recordOrigin></recordInfo></relatedItem><identifier type="istex">611BC9E52B694ADA5748DD91EEF5379215628568</identifier><identifier type="ark">ark:/67375/VQC-RCF0SGC7-H</identifier><identifier type="DOI">10.1023/A:1010537316758</identifier><identifier type="ArticleID">359773</identifier><identifier type="ArticleID">Art14</identifier><accessCondition type="use and reproduction" contentType="copyright">Kluwer Academic Publishers/Akadémiai Kiadó, 2001</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-3XSW68JL-F">springer</recordContentSource><recordOrigin>Kluwer Academic Publishers/Akadémiai Kiadó, 2001</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/VQC-RCF0SGC7-H/record.json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001660 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 001660 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Istex
|étape= Corpus
|type= RBID
|clé= ISTEX:611BC9E52B694ADA5748DD91EEF5379215628568
|texte= Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach
}}
| This area was generated with Dilib version V0.6.33. |  |