Search (20 results, page 1 of 1)

  • × theme_ss:"Visualisierung"
  • × type_ss:"el"
  1. Jaklitsch, M.: Informationsvisualisierung am Beispiel des Begriffs Informationskompetenz : eine szientometrische Untersuchung unter Verwendung von BibExcel und VOSviewer (2016) 0.00
    0.0032080694 = product of:
      0.02406052 = sum of:
        0.021100556 = weight(_text_:und in 3067) [ClassicSimilarity], result of:
          0.021100556 = score(doc=3067,freq=10.0), product of:
            0.06422601 = queryWeight, product of:
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.028978055 = queryNorm
            0.328536 = fieldWeight in 3067, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.046875 = fieldNorm(doc=3067)
        0.002959963 = product of:
          0.005919926 = sum of:
            0.005919926 = weight(_text_:information in 3067) [ClassicSimilarity], result of:
              0.005919926 = score(doc=3067,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.116372846 = fieldWeight in 3067, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3067)
          0.5 = coord(1/2)
      0.13333334 = coord(2/15)
    
    Abstract
    Zielsetzung - Aufgrund der rasch wachsenden Anzahl an Publikationen zur Informationskompetenz ergibt sich eine zunehmende Notwendigkeit von Überblicksarbeiten. Dieser Betrag hat das Ziel, mittels Science Mapping einen Überblick über die wissenschaftliche Literatur zu schaffen. Forschungsmethoden - Unter Verwendung von BibExcel und VOSviewer wurden 1589 wissenschaftliche Artikel analysiert und drei verschiedene Visualisierungen erstellt. Ergebnisse - Es gibt ein relativ großes internationales Autorennetzwerk, in welchem die meisten Hauptakteure miteinander in Verbindung stehen. Die wichtigsten Schwerpunkte sind: Vermittlung von Informationskompetenz im Hochschulbereich, Prozessmodelle zum Informationssuchverhalten, Phänomenographie und Informationskompetenz im beruflichen Umfeld. Schlussfolgerungen - Viele der Schwerpunkte wurden schon vereinzelt in Review-Artikeln genannt, aber noch nie via Science Mapping zusammen visualisiert. Somit ermöglicht diese Arbeit erstmalig ein »big picture« der Produktionslandschaft. Künftige Arbeiten könnten die Literatur mit anderen Science Mapping Tools bzw. Visualisierungstechniken untersuchen.
    Content
    Vgl.: https://yis.univie.ac.at/index.php/yis/article/view/1417/1251. Diesem Beitrag liegt folgende Abschlussarbeit zugrunde: Jaklitsch, Markus: Informationsvisualisierung am Beispiel des Begriffs Informationskompetenz: Eine szientometrische Untersuchung unter Verwendung von BibExcel und VOSviewer. Masterarbeit (MSc), Karl-Franzens-Universität Graz, 2015. Volltext: http://resolver.obvsg.at/urn:nbn:at:at-ubg:1-90404.
    Source
    Young information scientists. 1(2016), S.31-43
  2. Eckert, K: ¬The ICE-map visualization (2011) 0.00
    0.0025890246 = product of:
      0.019417683 = sum of:
        0.012581941 = weight(_text_:und in 4743) [ClassicSimilarity], result of:
          0.012581941 = score(doc=4743,freq=2.0), product of:
            0.06422601 = queryWeight, product of:
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.028978055 = queryNorm
            0.19590102 = fieldWeight in 4743, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.0625 = fieldNorm(doc=4743)
        0.006835742 = product of:
          0.013671484 = sum of:
            0.013671484 = weight(_text_:information in 4743) [ClassicSimilarity], result of:
              0.013671484 = score(doc=4743,freq=6.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.2687516 = fieldWeight in 4743, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0625 = fieldNorm(doc=4743)
          0.5 = coord(1/2)
      0.13333334 = coord(2/15)
    
    Abstract
    In this paper, we describe in detail the Information Content Evaluation Map (ICE-Map Visualization, formerly referred to as IC Difference Analysis). The ICE-Map Visualization is a visual data mining approach for all kinds of concept hierarchies that uses statistics about the concept usage to help a user in the evaluation and maintenance of the hierarchy. It consists of a statistical framework that employs the the notion of information content from information theory, as well as a visualization of the hierarchy and the result of the statistical analysis by means of a treemap.
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  3. Teutsch, K.: ¬Die Welt ist doch eine Scheibe : Google-Herausforderer eyePlorer (2009) 0.00
    0.0025089488 = product of:
      0.018817116 = sum of:
        0.017583797 = weight(_text_:und in 2678) [ClassicSimilarity], result of:
          0.017583797 = score(doc=2678,freq=40.0), product of:
            0.06422601 = queryWeight, product of:
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.028978055 = queryNorm
            0.27378 = fieldWeight in 2678, product of:
              6.3245554 = tf(freq=40.0), with freq of:
                40.0 = termFreq=40.0
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.01953125 = fieldNorm(doc=2678)
        0.0012333179 = product of:
          0.0024666358 = sum of:
            0.0024666358 = weight(_text_:information in 2678) [ClassicSimilarity], result of:
              0.0024666358 = score(doc=2678,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.048488684 = fieldWeight in 2678, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.01953125 = fieldNorm(doc=2678)
          0.5 = coord(1/2)
      0.13333334 = coord(2/15)
    
    Content
    "An einem trüben Novembertag 2008 sitzen zwei Männer an einem ovalen Konferenztisch. Sie befinden sich wie die meisten Geschäftstreibenden im Strudel der Finanzmärkte. Ihr Tisch steht im einzigen mehrstöckigen Nachwendebau der Berliner Karl-Marx-Allee. Links vom Fenster leuchtet die Spitze des Fernsehturms, rechts fällt der Blick auf kilometerlange Kachelfassaden. Die Verhandlungen mit den Investoren ziehen sich seit Wochen hin. Ein rhetorisches Ringen. Der Hirnforscher fragt: "Ist Wissen mit großem 'W' und wissen mit kleinem 'w' für Sie das Gleiche?" Der Vertriebsmann sagt: "Learntainment", "Knowledge Nuggets", "Mindmapping". Am Ende liegt ein unterschriebener Vertrag auf dem Tisch - an einem Tag, an dem Daimler laut über Kurzarbeit nachdenkt. Martin Hirsch und Ralf von Grafenstein genehmigen sich einen Piccolo. In der schwersten Wirtschaftskrise der Bundesrepublik haben sie für "eyePlorer" einen potenten Investor gefunden. Er hat die Tragweite ihrer Idee verstanden, und er hat begriffen: Die Welt ist eine Scheibe.
    Eine neue visuelle Ordnung Martin Hirsch ist der Enkel des Nobelpreisträgers Werner Heisenberg. Außerdem ist er Hirnforscher und beschäftigt sich seit Jahren mit der Frage: Was tut mein Kopf eigentlich, während ich hirnforsche? Ralf von Grafenstein ist Marketingexperte und spezialisiert auf Dienstleistungen im Internet. Zusammen haben sie also am 1. Dezember 2008 eine Firma in Berlin gegründet, deren Heiliger Gral besagte Scheibe ist, auf der - das ist die Idee - bald die ganze Welt, die Internetwelt zumindest, Platz finden soll. Die Scheibe heißt eyePlorer, was sich als Aufforderung an ihre Nutzer versteht. Die sollen auf einer neuartigen, eben scheibenförmigen Plattform die unermesslichen Datensätze des Internets in eine neue visuelle Ordnung bringen. Der Schlüssel dafür, da waren sich Hirsch und von Grafenstein sicher, liegt in der Hirnforschung, denn warum nicht die assoziativen Fähigkeiten des Menschen auf Suchmaschinen übertragen? Anbieter wie Google lassen von solchen Ansätzen bislang die Finger. Hier setzt man dafür auf Volltextprogramme, also sprachbegabte Systeme, die letztlich aber, genau wie die Schlagwortsuche, nur zu opak gerankten Linksammlungen führen. Weiter als auf Seite zwei des Suchergebnisses wagt sich der träge Nutzer meistens nicht vor. Weil sie niemals wahrgenommen wird, fällt eine Menge möglicherweise kostbare Information unter den Tisch.
    Skelett mit Sonnenbrille Hirsch sitzt in einem grell erleuchteten Konferenzraum. In der rechten Ecke steht ein Skelett, dem jemand eine Sonnenbrille aufgeklemmt hat. In der Hand hält Hirsch ein Modellgehirn, auf dem er im Rhythmus seines Sprachflusses mit den Fingern trommelt. Obwohl im Verlauf der nächsten Stunden erschreckend verwickelte Netzdiagramme zum Einsatz kommen, hält Hirsch sich an die Suggestivkraft des Bildes. Er sagt: "Das Primärerlebnis der Maschine ist bei Google das eines Jägers. Sie pirscht sich an eine Internetseite heran." Man denkt: "Genauso fühlt es sich an: Suchbegriff eingeben, 'enter' drücken, Website schießen!", schon kommt die Komplementärmetapher geschmeidig aus dem Köcher: Im Gegensatz zum Google-Jäger, sagt Hirsch, sei der eyePlorer ein Sammler, der stöbere, organisiere und dann von allem nasche. Hier werden Informationen, auf die handelsübliche Suchmaschinen nur verweisen, kulinarisch aufbereitet und zu Schwerpunkten verknüpft. Im Gegensatz zu ihren Vorgängern ist die Maschine ansatzweise intelligent. Sie findet im Laufe einer Sitzung heraus, worum es dem Benutzer geht, versteht den Zusammenhang von Suche und Inhalt und ist deshalb in der Lage, Empfehlungen auszusprechen.
    Einstein, Weizsäcker und Hitler Zu Demonstrationszwecken wird die eyePlorer-Scheibe an die Wand projiziert. Gibt man im kleinen Suchfeld in der Mitte den Namen Werner Heisenberg ein, verwandelt sich die Scheibe in einen Tortenboden. Die einzelnen Stücke entsprechen Kategorien wie "Person", "Technologie" oder "Organisation". Sie selbst sind mit bunten Knöpfen bedeckt, unter denen sich die Informationen verbergen. So kommt es, dass man beim Thema Heisenberg nicht nur auf die Kollegen Einstein, Weizsäcker und Schrödinger trifft, sondern auch auf Adolf Hitler. Ein Klick auf den entsprechenden Button stellt unter anderem heraus: Heisenberg kam 1933 unter Beschuss der SS, weil er sich nicht vor den Karren einer antisemitischen Physikbewegung spannen ließ. Nach diesem Prinzip spült die frei assoziierende Maschine vollautomatisch immer wieder neue Fakten an, um die der Nutzer zwar nicht gebeten hat, die ihn bei seiner Recherche aber möglicherweise unterstützen und die er später - die Maschine ist noch ausbaubedürftig - auch modellieren darf. Aber will man das, sich von einer Maschine beraten lassen? "Google ist wie ein Zoo", sekundiert Ralf von Grafenstein. "In einem Gehege steht eine Giraffe, im anderen ein Raubtier, aber die sind klar getrennt voneinander durch Gitter und Wege. Es gibt keine Möglichkeit, sie zusammen anzuschauen. Da kommen wir ins Spiel. Wir können Äpfel mit Birnen vergleichen!" Die Welt ist eine Scheibe oder die Scheibe eben eine Welt, auf der vieles mit vielem zusammenhängt und manches auch mit nichts. Der Vorteil dieser Maschine ist, dass sie in Zukunft Sinn stiften könnte, wo andere nur spröde auf Quellen verweisen. "Google ist ja ein unheimlich heterogenes Erlebnis mit ständigen Wartezeiten und Mausklicks dazwischen. Das kostet mich viel zu viel Metagedankenkraft", sagt Hirsch. "Wir wollten eine Maschine mit einer ästhetisch ansprechenden Umgebung bauen, aus der ich mich kaum wegbewege, denn sie liefert mir Informationen in meinen Gedanken hinein."
    Wenn die Maschine denkt Zur Hybris des Projekts passt, dass der eyePlorer ursprünglich HAL heißen sollte - wie der außer Rand und Band geratene Bordcomputer aus Kubricks "2001: Odyssee im Weltraum". Wenn man die Buchstaben aber jeweils um eine Alphabetposition nach rechts verrückt, ergibt sich IBM. Was passiert mit unserem Wissen, wenn die Maschine selbst anfängt zu denken? Ralf von Grafenstein macht ein ernstes Gesicht. "Es ist nicht unser Ansinnen, sie alleinzulassen. Es geht bei uns ja nicht nur darum, zu finden, sondern auch mitzumachen. Die Community ist wichtig. Der Dialog ist beiderseitig." Der Lotse soll in Form einer wachsamen Gemeinschaft also an Bord bleiben. Begünstigt wird diese Annahme auch durch die aufkommenden Anfasstechnologien, mit denen das iPhone derzeit so erfolgreich ist: "Allein zehn Prozent der menschlichen Gehirnleistung gehen auf den Pinzettengriff zurück." Martin Hirsch wundert sich, dass diese Erkenntnis von der IT-Branche erst jetzt berücksichtigt wird. Auf berührungssensiblen Bildschirmen sollen die Nutzer mit wenigen Handgriffen bald spielerisch Inhalte schaffen und dem System zur Verfügung stellen. So wird aus der Suchmaschine ein "Sparringspartner" und aus einem Informationsknopf ein "Knowledge Nugget". Wie auch immer man die Erkenntniszutaten des Internetgroßmarkts serviert: Wissen als Zeitwort ist ein länglicher Prozess. Im Moment sei die Maschine noch auf dem Stand eines Zweijährigen, sagen ihre Schöpfer. Sozialisiert werden soll sie demnächst im Internet, ihre Erziehung erfolgt dann durch die Nutzer. Als er Martin Hirsch mit seiner Scheibe zum ersten Mal gesehen habe, dachte Ralf von Grafenstein: "Das ist überfällig! Das wird kommen! Das muss raus!" Jetzt ist es da, klein, unschuldig und unscheinbar. Man findet es bei Google."
  4. Waechter, U.: Visualisierung von Netzwerkstrukturen (2002) 0.00
    0.0024217735 = product of:
      0.018163301 = sum of:
        0.012581941 = weight(_text_:und in 1735) [ClassicSimilarity], result of:
          0.012581941 = score(doc=1735,freq=2.0), product of:
            0.06422601 = queryWeight, product of:
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.028978055 = queryNorm
            0.19590102 = fieldWeight in 1735, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.0625 = fieldNorm(doc=1735)
        0.0055813594 = product of:
          0.011162719 = sum of:
            0.011162719 = weight(_text_:information in 1735) [ClassicSimilarity], result of:
              0.011162719 = score(doc=1735,freq=4.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.21943474 = fieldWeight in 1735, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0625 = fieldNorm(doc=1735)
          0.5 = coord(1/2)
      0.13333334 = coord(2/15)
    
    Abstract
    Das WWW entwickelte sich aus dem Bedürfnis, textuelle Information einfach und schnell zu durchforsten. Dabei entstand das Konzept des 'Hyperlinks', womit es möglich ist, Texte miteinander zu verknüpfen. Die Anzahl der Webseiten nahm mit der Verbreitung des WWW rapide zu. Das Problem heutzutage ist: Es gibt prinzipiell jede Art von Information im Internet, doch wie kommt man da dran?
  5. Palm, F.: QVIZ : Query and context based visualization of time-spatial cultural dynamics (2007) 0.00
    0.0022540248 = product of:
      0.03381037 = sum of:
        0.03381037 = sum of:
          0.010253613 = weight(_text_:information in 1289) [ClassicSimilarity], result of:
            0.010253613 = score(doc=1289,freq=6.0), product of:
              0.050870337 = queryWeight, product of:
                1.7554779 = idf(docFreq=20772, maxDocs=44218)
                0.028978055 = queryNorm
              0.20156369 = fieldWeight in 1289, product of:
                2.4494898 = tf(freq=6.0), with freq of:
                  6.0 = termFreq=6.0
                1.7554779 = idf(docFreq=20772, maxDocs=44218)
                0.046875 = fieldNorm(doc=1289)
          0.023556758 = weight(_text_:22 in 1289) [ClassicSimilarity], result of:
            0.023556758 = score(doc=1289,freq=2.0), product of:
              0.101476215 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.028978055 = queryNorm
              0.23214069 = fieldWeight in 1289, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=1289)
      0.06666667 = coord(1/15)
    
    Abstract
    QVIZ will research and create a framework for visualizing and querying archival resources by a time-space interface based on maps and emergent knowledge structures. The framework will also integrate social software, such as wikis, in order to utilize knowledge in existing and new communities of practice. QVIZ will lead to improved information sharing and knowledge creation, easier access to information in a user-adapted context and innovative ways of exploring and visualizing materials over time, between countries and other administrative units. The common European framework for sharing and accessing archival information provided by the QVIZ project will open a considerably larger commercial market based on archival materials as well as a richer understanding of European history.
    Content
    Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
  6. Wachsmann, L.: Entwurf und Implementierung eines Modells zur Visualisierung von OWL-Properties als Protégé-PlugIn mit Layoutalgorithmen aus Graphviz (2008) 0.00
    0.0011862369 = product of:
      0.017793551 = sum of:
        0.017793551 = weight(_text_:und in 4173) [ClassicSimilarity], result of:
          0.017793551 = score(doc=4173,freq=4.0), product of:
            0.06422601 = queryWeight, product of:
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.028978055 = queryNorm
            0.27704588 = fieldWeight in 4173, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.0625 = fieldNorm(doc=4173)
      0.06666667 = coord(1/15)
    
    Abstract
    Diese Diplomarbeit beschäftigt sich mit der Erstellung eines PlugIns für den Ontologie-Editor Protégé. Das PlugIn visualisiert Objekt-Properties als Verknüpfungen zwischen zwei OWL-Klassen. Als Ausgangspunkt für die Entwicklung dient das PlugIn OWLViz, das Vererbungshierarchien von OWL-Klassen als Graphen darstellt. Die Platzierung der Knoten und Kanten des Graphen wird von Algorithmen der Programmbibliothek Graphviz vorgenommen.
  7. Graphic details : a scientific study of the importance of diagrams to science (2016) 0.00
    9.825561E-4 = product of:
      0.014738342 = sum of:
        0.014738342 = sum of:
          0.002959963 = weight(_text_:information in 3035) [ClassicSimilarity], result of:
            0.002959963 = score(doc=3035,freq=2.0), product of:
              0.050870337 = queryWeight, product of:
                1.7554779 = idf(docFreq=20772, maxDocs=44218)
                0.028978055 = queryNorm
              0.058186423 = fieldWeight in 3035, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.7554779 = idf(docFreq=20772, maxDocs=44218)
                0.0234375 = fieldNorm(doc=3035)
          0.011778379 = weight(_text_:22 in 3035) [ClassicSimilarity], result of:
            0.011778379 = score(doc=3035,freq=2.0), product of:
              0.101476215 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.028978055 = queryNorm
              0.116070345 = fieldWeight in 3035, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0234375 = fieldNorm(doc=3035)
      0.06666667 = coord(1/15)
    
    Content
    As the team describe in a paper posted (http://arxiv.org/abs/1605.04951) on arXiv, they found that figures did indeed matter-but not all in the same way. An average paper in PubMed Central has about one diagram for every three pages and gets 1.67 citations. Papers with more diagrams per page and, to a lesser extent, plots per page tended to be more influential (on average, a paper accrued two more citations for every extra diagram per page, and one more for every extra plot per page). By contrast, including photographs and equations seemed to decrease the chances of a paper being cited by others. That agrees with a study from 2012, whose authors counted (by hand) the number of mathematical expressions in over 600 biology papers and found that each additional equation per page reduced the number of citations a paper received by 22%. This does not mean that researchers should rush to include more diagrams in their next paper. Dr Howe has not shown what is behind the effect, which may merely be one of correlation, rather than causation. It could, for example, be that papers with lots of diagrams tend to be those that illustrate new concepts, and thus start a whole new field of inquiry. Such papers will certainly be cited a lot. On the other hand, the presence of equations really might reduce citations. Biologists (as are most of those who write and read the papers in PubMed Central) are notoriously mathsaverse. If that is the case, looking in a physics archive would probably produce a different result.
    Dr Howe and his colleagues do, however, believe that the study of diagrams can result in new insights. A figure showing new metabolic pathways in a cell, for example, may summarise hundreds of experiments. Since illustrations can convey important scientific concepts in this way, they think that browsing through related figures from different papers may help researchers come up with new theories. As Dr Howe puts it, "the unit of scientific currency is closer to the figure than to the paper." With this thought in mind, the team have created a website (viziometrics.org (http://viziometrics.org/) ) where the millions of images sorted by their program can be searched using key words. Their next plan is to extract the information from particular types of scientific figure, to create comprehensive "super" figures: a giant network of all the known chemical processes in a cell for example, or the best-available tree of life. At just one such superfigure per paper, though, the citation records of articles containing such all-embracing diagrams may very well undermine the correlation that prompted their creation in the first place. Call it the ultimate marriage of chart and science.
  8. Kraker, P.; Kittel, C,; Enkhbayar, A.: Open Knowledge Maps : creating a visual interface to the world's scientific knowledge based on natural language processing (2016) 0.00
    6.2909705E-4 = product of:
      0.009436456 = sum of:
        0.009436456 = weight(_text_:und in 3205) [ClassicSimilarity], result of:
          0.009436456 = score(doc=3205,freq=2.0), product of:
            0.06422601 = queryWeight, product of:
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.028978055 = queryNorm
            0.14692576 = fieldWeight in 3205, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.216367 = idf(docFreq=13101, maxDocs=44218)
              0.046875 = fieldNorm(doc=3205)
      0.06666667 = coord(1/15)
    
    Content
    Beitrag in einem Themenschwerpunkt 'Computerlinguistik und Bibliotheken'. Vgl.: http://0277.ch/ojs/index.php/cdrs_0277/article/view/157/355.
  9. Fowler, R.H.; Wilson, B.A.; Fowler, W.A.L.: Information navigator : an information system using associative networks for display and retrieval (1992) 0.00
    5.2208843E-4 = product of:
      0.007831326 = sum of:
        0.007831326 = product of:
          0.015662652 = sum of:
            0.015662652 = weight(_text_:information in 919) [ClassicSimilarity], result of:
              0.015662652 = score(doc=919,freq=14.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.3078936 = fieldWeight in 919, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046875 = fieldNorm(doc=919)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    Document retrieval is a highly interactive process dealing with large amounts of information. Visual representations can provide both a means for managing the complexity of large information structures and an interface style well suited to interactive manipulation. The system we have designed utilizes visually displayed graphic structures and a direct manipulation interface style to supply an integrated environment for retrieval. A common visually displayed network structure is used for query, document content, and term relations. A query can be modified through direct manipulation of its visual form by incorporating terms from any other information structure the system displays. An associative thesaurus of terms and an inter-document network provide information about a document collection that can complement other retrieval aids. Visualization of these large data structures makes use of fisheye views and overview diagrams to help overcome some of the inherent difficulties of orientation and navigation in large information structures.
  10. Visual thesaurus (2005) 0.00
    5.095061E-4 = product of:
      0.0076425914 = sum of:
        0.0076425914 = product of:
          0.015285183 = sum of:
            0.015285183 = weight(_text_:information in 1292) [ClassicSimilarity], result of:
              0.015285183 = score(doc=1292,freq=30.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.3004734 = fieldWeight in 1292, product of:
                  5.477226 = tf(freq=30.0), with freq of:
                    30.0 = termFreq=30.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.03125 = fieldNorm(doc=1292)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    A visual thesaurus system and method for displaying a selected term in association with its one or more meanings, other words to which it is related, and further relationship information. The results of a search are presented in a directed graph that provides more information than an ordered list. When a user selects one of the results, the display reorganizes around the user's search allowing for further searches, without the interruption of going to additional pages.
    Content
    Traditional print reference guides often have two methods of finding information: an order (alphabetical for dictionaries and encyclopedias, by subject hierarchy in the case of thesauri) and indices (ordered lists, with a more complete listing of words and concepts, which refers back to original content from the main body of the book). A user of such traditional print reference guides who is looking for information will either browse through the ordered information in the main body of the reference book, or scan through the indices to find what is necessary. The advent of the computer allows for much more rapid electronic searches of the same information, and for multiple layers of indices. Users can either search through information by entering a keyword, or users can browse through the information through an outline index, which represents the information contained in the main body of the data. There are two traditional user interfaces for such applications. First, the user may type text into a search field and in response, a list of results is returned to the user. The user then selects a returned entry and may page through the resulting information. Alternatively, the user may choose from a list of words from an index. For example, software thesaurus applications, in which a user attempts to find synonyms, antonyms, homonyms, etc. for a selected word, are usually implemented using the conventional search and presentation techniques discussed above. The presentation of results only allows for a one-dimensional order of data at any one time. In addition, only a limited number of results can be shown at once, and selecting a result inevitably leads to another page-if the result is not satisfactory, the users must search again. Finally, it is difficult to present information about the manner in which the search results are related, or to present quantitative information about the results without causing confusion. Therefore, there exists a need for a multidimensional graphical display of information, in particular with respect to information relating to the meaning of words and their relationships to other words. There further exists a need to present large amounts of information in a way that can be manipulated by the user, without the user losing his place. And there exists a need for more fluid, intuitive and powerful thesaurus functionality that invites the exploration of language.
  11. Dushay, N.: Visualizing bibliographic metadata : a virtual (book) spine viewer (2004) 0.00
    3.9466174E-4 = product of:
      0.005919926 = sum of:
        0.005919926 = product of:
          0.011839852 = sum of:
            0.011839852 = weight(_text_:information in 1197) [ClassicSimilarity], result of:
              0.011839852 = score(doc=1197,freq=32.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.23274569 = fieldWeight in 1197, product of:
                  5.656854 = tf(freq=32.0), with freq of:
                    32.0 = termFreq=32.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0234375 = fieldNorm(doc=1197)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    User interfaces for digital information discovery often require users to click around and read a lot of text in order to find the text they want to read-a process that is often frustrating and tedious. This is exacerbated because of the limited amount of text that can be displayed on a computer screen. To improve the user experience of computer mediated information discovery, information visualization techniques are applied to the digital library context, while retaining traditional information organization concepts. In this article, the "virtual (book) spine" and the virtual spine viewer are introduced. The virtual spine viewer is an application which allows users to visually explore large information spaces or collections while also allowing users to hone in on individual resources of interest. The virtual spine viewer introduced here is an alpha prototype, presented to promote discussion and further work. Information discovery changed radically with the introduction of computerized library access catalogs, the World Wide Web and its search engines, and online bookstores. Yet few instances of these technologies provide a user experience analogous to walking among well-organized, well-stocked bookshelves-which many people find useful as well as pleasurable. To put it another way, many of us have heard or voiced complaints about the paucity of "online browsing"-but what does this really mean? In traditional information spaces such as libraries, often we can move freely among the books and other resources. When we walk among organized, labeled bookshelves, we get a sense of the information space-we take in clues, perhaps unconsciously, as to the scope of the collection, the currency of resources, the frequency of their use, etc. We also enjoy unexpected discoveries such as finding an interesting resource because library staff deliberately located it near similar resources, or because it was miss-shelved, or because we saw it on a bookshelf on the way to the water fountain.
    When our experience of information discovery is mediated by a computer, we neither move ourselves nor the monitor. We have only the computer's monitor to view, and the keyboard and/or mouse to manipulate what is displayed there. Computer interfaces often reduce our ability to get a sense of the contents of a library: we don't perceive the scope of the library: its breadth, (the quantity of materials/information), its density (how full the shelves are, how thorough the collection is for individual topics), or the general audience for the materials (e.g., whether the materials are appropriate for middle school students, college professors, etc.). Additionally, many computer interfaces for information discovery require users to scroll through long lists, to click numerous navigational links and to read a lot of text to find the exact text they want to read. Text features of resources are almost always presented alphabetically, and the number of items in these alphabetical lists sometimes can be very long. Alphabetical ordering is certainly an improvement over no ordering, but it generally has no bearing on features with an inherent non-alphabetical ordering (e.g., dates of historical events), nor does it necessarily group similar items together. Alphabetical ordering of resources is analogous to one of the most familiar complaints about dictionaries: sometimes you need to know how to spell a word in order to look up its correct spelling in the dictionary. Some have used technology to replicate the appearance of physical libraries, presenting rooms of bookcases and shelves of book spines in virtual 3D environments. This approach presents a problem, as few book spines can be displayed legibly on a monitor screen. This article examines the role of book spines, call numbers, and other traditional organizational and information discovery concepts, and integrates this knowledge with information visualization techniques to show how computers and monitors can meet or exceed similar information discovery methods. The goal is to tap the unique potentials of current information visualization approaches in order to improve information discovery, offer new services, and most important of all, improve user satisfaction. We need to capitalize on what computers do well while bearing in mind their limitations. The intent is to design GUIs to optimize utility and provide a positive experience for the user.
  12. Barton, P.: ¬A missed opportunity : why the benefits of information visualisation seem still out of sight (2005) 0.00
    3.9466174E-4 = product of:
      0.005919926 = sum of:
        0.005919926 = product of:
          0.011839852 = sum of:
            0.011839852 = weight(_text_:information in 1293) [ClassicSimilarity], result of:
              0.011839852 = score(doc=1293,freq=8.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.23274569 = fieldWeight in 1293, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1293)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    This paper aims to identify what information visualisation is and how in conjunction with the computer it can be used as a tool to expand understanding. It also seeks to explain how information visualisation has been fundamental to the development of the computer from its very early days to Apple's launch of the now ubiquitous W.I.M.P (Windows, Icon, Menu, Program) graphical user interface in 1984. An attempt is also made to question why after many years of progress and development though the late 1960s and 1970s, very little has changed in the way we interact with the data on our computers since the watershed of the Macintosh and in conclusion where the future of information visualisation may lie.
  13. Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.00
    3.48835E-4 = product of:
      0.0052325246 = sum of:
        0.0052325246 = product of:
          0.010465049 = sum of:
            0.010465049 = weight(_text_:information in 1211) [ClassicSimilarity], result of:
              0.010465049 = score(doc=1211,freq=36.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.20572007 = fieldWeight in 1211, product of:
                  6.0 = tf(freq=36.0), with freq of:
                    36.0 = termFreq=36.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.01953125 = fieldNorm(doc=1211)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
    From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
    Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.
  14. Collins, C.: WordNet explorer : applying visualization principles to lexical semantics (2006) 0.00
    2.6310782E-4 = product of:
      0.0039466172 = sum of:
        0.0039466172 = product of:
          0.0078932345 = sum of:
            0.0078932345 = weight(_text_:information in 1288) [ClassicSimilarity], result of:
              0.0078932345 = score(doc=1288,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.1551638 = fieldWeight in 1288, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0625 = fieldNorm(doc=1288)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    Interface designs for lexical databases in NLP have suffered from not following design principles developed in the information visualization research community. We present a design paradigm and show it can be used to generate visualizations which maximize the usability and utility ofWordNet. The techniques can be generally applied to other lexical databases used in NLP research.
  15. Munzner, T.: Interactive visualization of large graphs and networks (2000) 0.00
    1.8604532E-4 = product of:
      0.0027906797 = sum of:
        0.0027906797 = product of:
          0.0055813594 = sum of:
            0.0055813594 = weight(_text_:information in 4746) [ClassicSimilarity], result of:
              0.0055813594 = score(doc=4746,freq=4.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.10971737 = fieldWeight in 4746, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.03125 = fieldNorm(doc=4746)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable.
  16. Linden, E.J. van der; Vliegen, R.; Wijk, J.J. van: Visual Universal Decimal Classification (2007) 0.00
    1.6444239E-4 = product of:
      0.0024666358 = sum of:
        0.0024666358 = product of:
          0.0049332716 = sum of:
            0.0049332716 = weight(_text_:information in 548) [ClassicSimilarity], result of:
              0.0049332716 = score(doc=548,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.09697737 = fieldWeight in 548, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=548)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Content
    Beitrag anlässlich des 'UDC Seminar: Information Access for the Global Community, The Hague, 4-5 June 2007'. - Vgl.: http://www.udcc.org/seminar07/presentations/magnaview.pdf.
  17. Xiaoyue M.; Cahier, J.-P.: Iconic categorization with knowledge-based "icon systems" can improve collaborative KM (2011) 0.00
    1.6444239E-4 = product of:
      0.0024666358 = sum of:
        0.0024666358 = product of:
          0.0049332716 = sum of:
            0.0049332716 = weight(_text_:information in 4837) [ClassicSimilarity], result of:
              0.0049332716 = score(doc=4837,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.09697737 = fieldWeight in 4837, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4837)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    Icon system could represent an efficient solution for collective iconic categorization of knowledge by providing graphical interpretation. Their pictorial characters assist visualizing the structure of text to become more understandable beyond vocabulary obstacle. In this paper we are proposing a Knowledge Engineering (KM) based iconic representation approach. We assume that these systematic icons improve collective knowledge management. Meanwhile, text (constructed under our knowledge management model - Hypertopic) helps to reduce the diversity of graphical understanding belonging to different users. This "position paper" also prepares to demonstrate our hypothesis by an "iconic social tagging" experiment which is to be accomplished in 2011 with UTT students. We describe the "socio semantic web" information portal involved in this project, and a part of the icons already designed for this experiment in Sustainability field. We have reviewed existing theoretical works on icons from various origins, which can be used to lay the foundation of robust "icons systems".
  18. Cao, N.; Sun, J.; Lin, Y.-R.; Gotz, D.; Liu, S.; Qu, H.: FacetAtlas : Multifaceted visualization for rich text corpora (2010) 0.00
    1.6444239E-4 = product of:
      0.0024666358 = sum of:
        0.0024666358 = product of:
          0.0049332716 = sum of:
            0.0049332716 = weight(_text_:information in 3366) [ClassicSimilarity], result of:
              0.0049332716 = score(doc=3366,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.09697737 = fieldWeight in 3366, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3366)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.
  19. Choi, I.: Visualizations of cross-cultural bibliographic classification : comparative studies of the Korean Decimal Classification and the Dewey Decimal Classification (2017) 0.00
    1.6444239E-4 = product of:
      0.0024666358 = sum of:
        0.0024666358 = product of:
          0.0049332716 = sum of:
            0.0049332716 = weight(_text_:information in 3869) [ClassicSimilarity], result of:
              0.0049332716 = score(doc=3869,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.09697737 = fieldWeight in 3869, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3869)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    The changes in KO systems induced by sociocultural influences may include those in both classificatory principles and cultural features. The proposed study will examine the Korean Decimal Classification (KDC)'s adaptation of the Dewey Decimal Classification (DDC) by comparing the two systems. This case manifests the sociocultural influences on KOSs in a cross-cultural context. Therefore, the study aims at an in-depth investigation of sociocultural influences by situating a KOS in a cross-cultural environment and examining the dynamics between two classification systems designed to organize information resources in two distinct sociocultural contexts. As a preceding stage of the comparison, the analysis was conducted on the changes that result from the meeting of different sociocultural feature in a descriptive method. The analysis aims to identify variations between the two schemes in comparison of the knowledge structures of the two classifications, in terms of the quantity of class numbers that represent concepts and their relationships in each of the individual main classes. The most effective analytic strategy to show the patterns of the comparison was visualizations of similarities and differences between the two systems. Increasing or decreasing tendencies in the class through various editions were analyzed. Comparing the compositions of the main classes and distributions of concepts in the KDC and DDC discloses the differences in their knowledge structures empirically. This phase of quantitative analysis and visualizing techniques generates empirical evidence leading to interpretation.
  20. Seeliger, F.: ¬A tool for systematic visualization of controlled descriptors and their relation to others as a rich context for a discovery system (2015) 0.00
    1.3155391E-4 = product of:
      0.0019733086 = sum of:
        0.0019733086 = product of:
          0.0039466172 = sum of:
            0.0039466172 = weight(_text_:information in 2547) [ClassicSimilarity], result of:
              0.0039466172 = score(doc=2547,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.0775819 = fieldWeight in 2547, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.03125 = fieldNorm(doc=2547)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    The discovery service (a search engine and service called WILBERT) used at our library at the Technical University of Applied Sciences Wildau (TUAS Wildau) is comprised of more than 8 million items. If we were to record all licensed publications in this tool to a higher level of articles, including their bibliographic records and full texts, we would have a holding estimated at a hundred million documents. A lot of features, such as ranking, autocompletion, multi-faceted classification, refining opportunities reduce the number of hits. However, it is not enough to give intuitive support for a systematic overview of topics related to documents in the library. John Naisbitt once said: "We are drowning in information, but starving for knowledge." This quote is still very true today. Two years ago, we started to develop micro thesauri for MINT topics in order to develop an advanced indexing of the library stock. We use iQvoc as a vocabulary management system to create the thesaurus. It provides an easy-to-use browser interface that builds a SKOS thesaurus in the background. The purpose of this is to integrate the thesauri in WILBERT in order to offer a better subject-related search. This approach especially supports first-year students by giving them the possibility to browse through a hierarchical alignment of a subject, for instance, logistics or computer science, and thereby discover how the terms are related. It also supports the students with an insight into established abbreviations and alternative labels. Students at the TUAS Wildau were involved in the developmental process of the software regarding the interface and functionality of iQvoc. The first steps have been taken and involve the inclusion of 3000 terms in our discovery tool WILBERT.