Search (20 results, page 1 of 1)

  • × type_ss:"el"
  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.01 
    0.008180852 = product of:
  0.061356384 = sum of:
    0.026966918 = weight(_text_:und in 4324) [ClassicSimilarity], result of:
      0.026966918 = score(doc=4324,freq=12.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.41987535 = fieldWeight in 4324, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4324)
    0.034389466 = sum of:
      0.00690658 = weight(_text_:information in 4324) [ClassicSimilarity], result of:
        0.00690658 = score(doc=4324,freq=2.0), product of:
          0.050870337 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.028978055 = queryNorm
          0.13576832 = fieldWeight in 4324, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.0546875 = fieldNorm(doc=4324)
      0.027482886 = weight(_text_:22 in 4324) [ClassicSimilarity], result of:
        0.027482886 = score(doc=4324,freq=2.0), product of:
          0.101476215 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.028978055 = queryNorm
          0.2708308 = fieldWeight in 4324, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=4324)
  0.13333334 = coord(2/15)
    Abstract
    Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.
    Date
    11. 2.2011 18:22:25
  2. Gillitzer, B.: Yewno (2017) 0.00 
    0.0040712897 = product of:
  0.03053467 = sum of:
    0.022682417 = weight(_text_:und in 3447) [ClassicSimilarity], result of:
      0.022682417 = score(doc=3447,freq=26.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.3531656 = fieldWeight in 3447, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.03125 = fieldNorm(doc=3447)
    0.0078522535 = product of:
      0.015704507 = sum of:
        0.015704507 = weight(_text_:22 in 3447) [ClassicSimilarity], result of:
          0.015704507 = score(doc=3447,freq=2.0), product of:
            0.101476215 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.028978055 = queryNorm
            0.15476047 = fieldWeight in 3447, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=3447)
      0.5 = coord(1/2)
  0.13333334 = coord(2/15)
    Abstract
    Yewno findet Themen und Konzepte (Suchbegriffe und ihre Abstraktionen) in englischsprachigen digitalen Texten mit Methoden des maschinellen Lernens und der künstlichen Intelligenz. Als Ergebnis Ihrer Suchanfrage werden die Konzepte, die Ihre Anfrage betreffen, in vielfältigen sachlichen Beziehungen als graphisches Netzwerk präsentiert, über das Sie einfach navigieren können. Auch versteckte thematische Beziehungen werden hier sichtbar gemacht, die vom Bekannten zu neuen Entdeckungen führen. Im Rahmen einer Pilotphase können Sie über einen interdisziplinären Ausschnitt aus aktuellen englischsprachigen Fachzeitschriften verschiedenster Fachgebiete recherchieren. Die zu den Themen gehörigen Artikel werden in Ausschnitten unmittelbar angezeigt und können in den meisten Fällen direkt als Volltext aufgerufen werden.
    "Die Bayerische Staatsbibliothek testet den semantischen "Discovery Service" Yewno als zusätzliche thematische Suchmaschine für digitale Volltexte. Der Service ist unter folgendem Link erreichbar: https://www.bsb-muenchen.de/recherche-und-service/suchen-und-finden/yewno/. Das Identifizieren von Themen, um die es in einem Text geht, basiert bei Yewno alleine auf Methoden der künstlichen Intelligenz und des maschinellen Lernens. Dabei werden sie nicht - wie bei klassischen Katalogsystemen - einem Text als Ganzem zugeordnet, sondern der jeweiligen Textstelle. Die Eingabe eines Suchwortes bzw. Themas, bei Yewno "Konzept" genannt, führt umgehend zu einer grafischen Darstellung eines semantischen Netzwerks relevanter Konzepte und ihrer inhaltlichen Zusammenhänge. So ist ein Navigieren über thematische Beziehungen bis hin zu den Fundstellen im Text möglich, die dann in sogenannten Snippets angezeigt werden. In der Test-Anwendung der Bayerischen Staatsbibliothek durchsucht Yewno aktuell 40 Millionen englischsprachige Dokumente aus Publikationen namhafter Wissenschaftsverlage wie Cambridge University Press, Oxford University Press, Wiley, Sage und Springer, sowie Dokumente, die im Open Access verfügbar sind. Nach der dreimonatigen Testphase werden zunächst die Rückmeldungen der Nutzer ausgewertet. Ob und wann dann der Schritt von der klassischen Suchmaschine zum semantischen "Discovery Service" kommt und welche Bedeutung Anwendungen wie Yewno in diesem Zusammenhang einnehmen werden, ist heute noch nicht abzusehen. Die Software Yewno wurde vom gleichnamigen Startup in Zusammenarbeit mit der Stanford University entwickelt, mit der auch die Bayerische Staatsbibliothek eng kooperiert. [Inetbib-Posting vom 22.02.2017].
    Date
    22. 2.2017 10:16:49
    Source
    https://www.bsb-muenchen.de/recherche-und-service/suchen-und-finden/yewno/
  3. Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006) 0.00 
    0.001743085 = product of:
  0.026146274 = sum of:
    0.026146274 = sum of:
      0.010441768 = weight(_text_:information in 1163) [ClassicSimilarity], result of:
        0.010441768 = score(doc=1163,freq=14.0), product of:
          0.050870337 = queryWeight, product of:
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.028978055 = queryNorm
          0.20526241 = fieldWeight in 1163, product of:
            3.7416575 = tf(freq=14.0), with freq of:
              14.0 = termFreq=14.0
            1.7554779 = idf(docFreq=20772, maxDocs=44218)
            0.03125 = fieldNorm(doc=1163)
      0.015704507 = weight(_text_:22 in 1163) [ClassicSimilarity], result of:
        0.015704507 = score(doc=1163,freq=2.0), product of:
          0.101476215 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.028978055 = queryNorm
          0.15476047 = fieldWeight in 1163, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=1163)
  0.06666667 = coord(1/15)
    Abstract
    This paper addresses the problem of information discovery in large collections of text. For users, one of the key problems in working with such collections is determining where to focus their attention. In selecting documents for examination, users must be able to formulate reasonably precise queries. Queries that are too broad will greatly reduce the efficiency of information discovery efforts by overwhelming the users with peripheral information. In order to formulate efficient queries, a mechanism is needed to automatically alert users regarding potentially interesting information contained within the collection. This paper presents the results of an experiment designed to test one approach to generation of such alerts. The technique of latent semantic indexing (LSI) is used to identify relationships among entities of interest. Entity extraction software is used to pre-process the text of the collection so that the LSI space contains representation vectors for named entities in addition to those for individual terms. In the LSI space, the cosine of the angle between the representation vectors for two entities captures important information regarding the degree of association of those two entities. For appropriate choices of entities, determining the entity pairs with the highest mutual cosine values yields valuable information regarding the contents of the text collection. The test database used for the experiment consists of 150,000 news articles. The proposed approach for alert generation is tested using a counterterrorism analysis example. The approach is shown to have significant potential for aiding users in rapidly focusing on information of potential importance in large text collections. The approach also has value in identifying possible use of aliases.
    Source
    Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]
  4. Kasprzik, A.; Kett, J.: Vorschläge für eine Weiterentwicklung der Sacherschließung und Schritte zur fortgesetzten strukturellen Aufwertung der GND (2018) 0.00 
    0.0017387326 = product of:
  0.026080986 = sum of:
    0.026080986 = weight(_text_:und in 4599) [ClassicSimilarity], result of:
      0.026080986 = score(doc=4599,freq=22.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.40608138 = fieldWeight in 4599, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4599)
  0.06666667 = coord(1/15)
    Abstract
    Aufgrund der fortgesetzten Publikationsflut stellt sich immer dringender die Frage, wie die Schwellen für die Titel- und Normdatenpflege gesenkt werden können - sowohl für die intellektuelle als auch die automatisierte Sacherschließung. Zu einer Verbesserung der Daten- und Arbeitsqualität in der Sacherschließung kann beigetragen werden a) durch eine flexible Visualisierung der Gemeinsamen Normdatei (GND) und anderer Wissensorganisationssysteme, so dass deren Graphstruktur intuitiv erfassbar wird, und b) durch eine investigative Analyse ihrer aktuellen Struktur und die Entwicklung angepasster automatisierter Methoden zur Ermittlung und Korrektur fehlerhafter Muster. Die Deutsche Nationalbibliothek (DNB) prüft im Rahmen des GND-Entwicklungsprogramms 2017-2021, welche Bedingungen für eine fruchtbare community-getriebene Open-Source-Entwicklung entsprechender Werkzeuge gegeben sein müssen. Weiteres Potential steckt in einem langfristigen Übergang zu einer Darstellung von Titel- und Normdaten in Beschreibungssprachen im Sinne des Semantic Web (RDF; OWL, SKOS). So profitiert die GND von der Interoperabilität mit anderen kontrollierten Vokabularen und von einer erleichterten Interaktion mit anderen Fach-Communities und kann umgekehrt auch außerhalb des Bibliothekswesens zu einem noch attraktiveren Wissensorganisationssystem werden. Darüber hinaus bieten die Ansätze aus dem Semantic Web die Möglichkeit, stärker formalisierte, strukturierende Satellitenvokabulare rund um die GND zu entwickeln. Daraus ergeben sich nicht zuletzt auch neue Perspektiven für die automatisierte Sacherschließung. Es wäre lohnend, näher auszuloten, wie und inwieweit semantisch-logische Verfahren den bestehenden Methodenmix bereichern können.
  5. Mandalka, M.: Open semantic search zum unabhängigen und datenschutzfreundlichen Erschliessen von Dokumenten (2015) 0.00 
    0.0017228533 = product of:
  0.025842797 = sum of:
    0.025842797 = weight(_text_:und in 2133) [ClassicSimilarity], result of:
      0.025842797 = score(doc=2133,freq=60.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.40237278 = fieldWeight in 2133, product of:
          7.745967 = tf(freq=60.0), with freq of:
            60.0 = termFreq=60.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0234375 = fieldNorm(doc=2133)
  0.06666667 = coord(1/15)
    Abstract
    Ob grösserer Leak oder Zusammenwürfeln oder (wieder) Erschliessen umfangreicherer (kollaborativer) Recherche(n) oder Archive: Immer öfter müssen im Journalismus größere Datenberge und Dokumentenberge erschlossen werden. In eine Suchmaschine integrierte Analyse-Tools helfen (halb)automatisch.
    Content
    "Open Semantic Desktop Search Zur Tagung des Netzwerk Recherche ist die Desktop Suchmaschine Open Semantic Desktop Search zum unabhängigen und datenschutzfreundlichen Erschliessen und Analysieren von Dokumentenbergen nun erstmals auch als deutschsprachige Version verfügbar. Dank mächtiger Open Source Basis kann die auf Debian GNU/Linux und Apache Solr basierende freie Software als unter Linux, Windows oder Mac lauffähige virtuelle Maschine kostenlos heruntergeladen, genutzt, weitergegeben und weiterentwickelt werden. Dokumentenberge erschliessen Ob grösserer Leak oder Zusammenwürfeln oder (wieder) Erschliessen umfangreicherer (kollaborativer) Recherche(n) oder Archive: Hin und wieder müssen größere Datenberge bzw. Dokumentenberge erschlossen werden, die so viele Dokumente enthalten, dass Mensch diese Masse an Dokumenten nicht mehr alle nacheinander durchschauen und einordnen kann. Auch bei kontinuierlicher Recherche zu Fachthemen sammeln sich mit der Zeit größere Mengen digitalisierter oder digitaler Dokumente zu grösseren Datenbergen an, die immer weiter wachsen und deren Informationen mit einer Suchmaschine für das Archiv leichter auffindbar bleiben. Moderne Tools zur Datenanalyse in Verbindung mit Enterprise Search Suchlösungen und darauf aufbauender Recherche-Tools helfen (halb)automatisch.
    Unabhängiges Durchsuchen und Analysieren grosser Datenmengen Damit können investigativ arbeitende Journalisten selbstständig und auf eigener Hardware datenschutzfreundlich hunderte, tausende, hunderttausende oder gar Millionen von Dokumenten oder hunderte Megabyte, Gigabytes oder gar einige Terabytes an Daten mit Volltextsuche durchsuchbar machen. Automatische Datenanreicherung und Erschliessung mittels Hintergrundwissen Zudem wird anhand von konfigurierbaren Hintergrundwissen automatisch eine interaktive Navigation zu in Dokumenten enthaltenen Namen von Bundestagsabgeordneten oder Orten in Deutschland generiert oder anhand Textmustern strukturierte Informationen wie Geldbeträge extrahiert. Mittels Named Entities Manager für Personen, Organisationen, Begriffe und Orte können eigene Rechercheschwerpunkte konfiguriert werden, aus denen dann automatisch eine interaktive Navigation (Facettensuche) und aggregierte Übersichten generiert werden. Automatische Datenvisualisierung Diese lassen sich auch visualisieren: So z.B. die zeitliche Verteilung von Suchergebnissen als Trand Diagramm oder durch gleichzeitige Nennung in Dokumenten abgeleitete Verbindungen als Netzwerk bzw. Graph.
    Automatische Texterkennung (OCR) Dokumente, die nicht im Textformat, sondern als Grafiken vorliegen, wie z.B. Scans werden automatisch durch automatische Texterkennung (OCR) angereichert und damit auch der extrahierte Text durchsuchbar. Auch für eingebettete Bilddateien bzw. Scans innerhalb von PDF-Dateien. Unscharfe Suche mit Listen Ansonsten ist auch das Recherche-Tool bzw. die Such-Applikation "Suche mit Listen" integriert, mit denen sich schnell und komfortabel abgleichen lässt, ob es zu den einzelnen Einträgen in Listen jeweils Treffer in der durchsuchbaren Dokumentensammlung gibt. Mittels unscharfer Suche findet das Tool auch Ergebnisse, die in fehlerhaften oder unterschiedlichen Schreibweisen vorliegen. Semantische Suche und Textmining Im Recherche, Textanalyse und Document Mining Tutorial zu den enthaltenen Recherche-Tools und verschiedenen kombinierten Methoden zur Datenanalyse, Anreicherung und Suche wird ausführlicher beschrieben, wie auch eine große heterogene und unstrukturierte Dokumentensammlung bzw. eine grosse Anzahl von Dokumenten in verschiedenen Formaten leicht durchsucht und analysiert werden kann.
    Virtuelle Maschine für mehr Plattformunabhängigkeit Die nun auch deutschsprachig verfügbare und mit deutschen Daten wie Ortsnamen oder Bundestagsabgeordneten vorkonfigurierte virtuelle Maschine Open Semantic Desktop Search ermöglicht nun auch auf einzelnen Desktop Computern oder Notebooks mit Windows oder iOS (Mac) die Suche und Analyse von Dokumenten mit der Suchmaschine Open Semantic Search. Als virtuelle Maschine (VM) lässt sich die Suchmaschine Open Semantic Search nicht nur für besonders sensible Dokumente mit dem verschlüsselten Live-System InvestigateIX als abgeschottetes System auf verschlüsselten externen Datenträgern installieren, sondern als virtuelle Maschine für den Desktop auch einfach unter Windows oder auf einem Mac in eine bzgl. weiterer Software und Daten bereits existierende Systemumgebung integrieren, ohne hierzu auf einen (für gemeinsame Recherchen im Team oder für die Redaktion auch möglichen) Suchmaschinen Server angewiesen zu sein. Datenschutz & Unabhängigkeit: Grössere Unabhängigkeit von zentralen IT-Infrastrukturen für unabhängigen investigativen Datenjournalismus Damit ist investigative Recherche weitmöglichst unabhängig möglich: ohne teure, zentrale und von Administratoren abhängige Server, ohne von der Dokumentenanzahl abhängige teure Software-Lizenzen, ohne Internet und ohne spionierende Cloud-Dienste. Datenanalyse und Suche finden auf dem eigenen Computer statt, nicht wie bei vielen anderen Lösungen in der sogenannten Cloud."
    Source
    http://www.linux-community.de/Internal/Nachrichten/Open-Semantic-Search-zum-unabhaengigen-und-datenschutzfreundlichen-Erschliessen-von-Dokumenten
  6. Hoppe, T.: Semantische Filterung : ein Werkzeug zur Steigerung der Effizienz im Wissensmanagement (2013) 0.00 
    0.0014528375 = product of:
  0.02179256 = sum of:
    0.02179256 = weight(_text_:und in 2245) [ClassicSimilarity], result of:
      0.02179256 = score(doc=2245,freq=6.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.33931053 = fieldWeight in 2245, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0625 = fieldNorm(doc=2245)
  0.06666667 = coord(1/15)
    Abstract
    Dieser Artikel adressiert einen Randbereich des Wissensmanagements: die Schnittstelle zwischen Unternehmens-externen Informationen im Internet und den Leistungsprozessen eines Unternehmens. Diese Schnittstelle ist besonders für Unternehmen von Interesse, deren Leistungsprozesse von externen Informationen abhängen und die auf diese Prozesse angewiesen sind. Wir zeigen an zwei Fallbeispielen, dass die inhaltliche Filterung von Informationen beim Eintritt ins Unternehmen ein wichtiges Werkzeug darstellt, um daran anschließende Wissens- und Informationsmanagementprozesse effizient zu gestalten.
  7. Tudhope, D.; Alani, H.; Jones, C.: Augmenting thesaurus relationships : possibilities for retrieval (2001) 0.00 
    0.00137738 = product of:
  0.010330349 = sum of:
    0.007863713 = weight(_text_:und in 1520) [ClassicSimilarity], result of:
      0.007863713 = score(doc=1520,freq=2.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.12243814 = fieldWeight in 1520, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1520)
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 1520) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=1520,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 1520, 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=1520)
      0.5 = coord(1/2)
  0.13333334 = coord(2/15)
    Source
    Journal of digital information. 1(2001) no.8
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  8. ALA / Subcommittee on Subject Relationships/Reference Structures: Final Report to the ALCTS/CCS Subject Analysis Committee (1997) 0.00 
    0.0011943852 = product of:
  0.008957889 = sum of:
    0.005504599 = weight(_text_:und in 1800) [ClassicSimilarity], result of:
      0.005504599 = score(doc=1800,freq=2.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.085706696 = fieldWeight in 1800, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.02734375 = fieldNorm(doc=1800)
    0.00345329 = product of:
      0.00690658 = sum of:
        0.00690658 = weight(_text_:information in 1800) [ClassicSimilarity], result of:
          0.00690658 = score(doc=1800,freq=8.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.13576832 = fieldWeight in 1800, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1800)
      0.5 = coord(1/2)
  0.13333334 = coord(2/15)
    Abstract
    The SAC Subcommittee on Subject Relationships/Reference Structures was authorized at the 1995 Midwinter Meeting and appointed shortly before Annual Conference. Its creation was one result of a discussion of how (and why) to promote the display and use of broader-term subject heading references, and its charge reads as follows: To investigate: (1) the kinds of relationships that exist between subjects, the display of which are likely to be useful to catalog users; (2) how these relationships are or could be recorded in authorities and classification formats; (3) options for how these relationships should be presented to users of online and print catalogs, indexes, lists, etc. By the summer 1996 Annual Conference, make some recommendations to SAC about how to disseminate the information and/or implement changes. At that time assess the need for additional time to investigate these issues. The Subcommittee's work on each of the imperatives in the charge was summarized in a report issued at the 1996 Annual Conference (Appendix A). Highlights of this work included the development of a taxonomy of 165 subject relationships; a demonstration that, using existing MARC coding, catalog systems could be programmed to generate references they do not currently support; and an examination of reference displays in several CD-ROM database products. Since that time, work has continued on identifying term relationships and display options; on tracking research, discussion, and implementation of subject relationships in information systems; and on compiling a list of further research needs.
    Content
    Enthält: Appendix A: Subcommittee on Subject Relationships/Reference Structures - REPORT TO THE ALCTS/CCS SUBJECT ANALYSIS COMMITTEE - July 1996 Appendix B (part 1): Taxonomy of Subject Relationships. Compiled by Dee Michel with the assistance of Pat Kuhr - June 1996 draft (alphabetical display) (Separat in: http://web2.ala.org/ala/alctscontent/CCS/committees/subjectanalysis/subjectrelations/msrscu2.pdf) Appendix B (part 2): Taxonomy of Subject Relationships. Compiled by Dee Michel with the assistance of Pat Kuhr - June 1996 draft (hierarchical display) Appendix C: Checklist of Candidate Subject Relationships for Information Retrieval. Compiled by Dee Michel, Pat Kuhr, and Jane Greenberg; edited by Greg Wool - June 1997 Appendix D: Review of Reference Displays in Selected CD-ROM Abstracts and Indexes by Harriette Hemmasi and Steven Riel Appendix E: Analysis of Relationships in Six LC Subject Authority Records by Harriette Hemmasi and Gary Strawn Appendix F: Report of a Preliminary Survey of Subject Referencing in OPACs by Gregory Wool Appendix G: LC Subject Referencing in OPACs--Why Bother? by Gregory Wool Appendix H: Research Needs on Subject Relationships and Reference Structures in Information Access compiled by Jane Greenberg and Steven Riel with contributions from Dee Michel and others edited by Gregory Wool Appendix I: Bibliography on Subject Relationships compiled mostly by Dee Michel with additional contributions from Jane Greenberg, Steven Riel, and Gregory Wool
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  9. Michel, D.: Taxonomy of Subject Relationships (1997) 0.00 
    0.0010484952 = product of:
  0.015727427 = sum of:
    0.015727427 = weight(_text_:und in 5346) [ClassicSimilarity], result of:
      0.015727427 = score(doc=5346,freq=2.0), product of:
        0.06422601 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.028978055 = queryNorm
        0.24487628 = fieldWeight in 5346, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.078125 = fieldNorm(doc=5346)
  0.06666667 = coord(1/15)
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  10. 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.
  11. Celik, I.; Abel, F.; Siehndel, P.: Adaptive faceted search on Twitter (2011) 0.00 
    3.7209064E-4 = product of:
  0.0055813594 = sum of:
    0.0055813594 = product of:
      0.011162719 = sum of:
        0.011162719 = weight(_text_:information in 2221) [ClassicSimilarity], result of:
          0.011162719 = score(doc=2221,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.21943474 = fieldWeight in 2221, 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=2221)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial efforts and users often have to scan information streams by hand. In this paper, we approach this problem by means of faceted search. We propose strategies for inferring facets and facet values on Twitter by enriching the semantics of individual Twitter messages and present di erent methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective.
  12. 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.
  13. Mäkelä, E.; Hyvönen, E.; Saarela, S.; Vilfanen, K.: Application of ontology techniques to view-based semantic serach and browsing (2012) 0.00 
    2.79068E-4 = product of:
  0.0041860198 = sum of:
    0.0041860198 = product of:
      0.0083720395 = sum of:
        0.0083720395 = weight(_text_:information in 3264) [ClassicSimilarity], result of:
          0.0083720395 = score(doc=3264,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.16457605 = fieldWeight in 3264, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3264)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    We scho how the beenfits of the view-based search method, developed within the information retrieval community, can be extended with ontology-based search, developed within the Semantic Web community, and with semantic recommendations. As a proof of the concept, we have implemented an ontology-and view-based search engine and recommendations system Ontogaotr for RDF(S) repositories. Ontogator is innovative in two ways. Firstly, the RDFS.based ontologies used for annotating metadata are used in the user interface to facilitate view-based information retrieval. The views provide the user with an overview of the repositorys contents and a vocabulary for expressing search queries. Secondlyy, a semantic browsing function is provided by a recommender system. This system enriches instance level metadata by ontologies and provides the user with links to semantically related relevant resources. The semantic linkage is specified in terms of logical rules. To illustrate and discuss the ideas, a deployed application of Ontogator to a photo repository of the Helsinki University Museum is presented.
  14. Jansen, B.; Browne, G.M.: Navigating information spaces : index / mind map / topic map? (2021) 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 436) [ClassicSimilarity], result of:
          0.0078932345 = score(doc=436,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.1551638 = fieldWeight in 436, 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=436)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
  15. Oard, D.W.: Alternative approaches for cross-language text retrieval (1997) 0.00 
    2.5739305E-4 = product of:
  0.0038608958 = sum of:
    0.0038608958 = product of:
      0.0077217915 = sum of:
        0.0077217915 = weight(_text_:information in 1164) [ClassicSimilarity], result of:
          0.0077217915 = score(doc=1164,freq=10.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.1517936 = fieldWeight in 1164, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1164)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    The explosive growth of the Internet and other sources of networked information have made automatic mediation of access to networked information sources an increasingly important problem. Much of this information is expressed as electronic text, and it is becoming practical to automatically convert some printed documents and recorded speech to electronic text as well. Thus, automated systems capable of detecting useful documents are finding widespread application. With even a small number of languages it can be inconvenient to issue the same query repeatedly in every language, so users who are able to read more than one language will likely prefer a multilingual text retrieval system over a collection of monolingual systems. And since reading ability in a language does not always imply fluent writing ability in that language, such users will likely find cross-language text retrieval particularly useful for languages in which they are less confident of their ability to express their information needs effectively. The use of such systems can be also be beneficial if the user is able to read only a single language. For example, when only a small portion of the document collection will ever be examined by the user, performing retrieval before translation can be significantly more economical than performing translation before retrieval. So when the application is sufficiently important to justify the time and effort required for translation, those costs can be minimized if an effective cross-language text retrieval system is available. Even when translation is not available, there are circumstances in which cross-language text retrieval could be useful to a monolingual user. For example, a researcher might find a paper published in an unfamiliar language useful if that paper contains references to works by the same author that are in the researcher's native language.
    Multilingual text retrieval can be defined as selection of useful documents from collections that may contain several languages (English, French, Chinese, etc.). This formulation allows for the possibility that individual documents might contain more than one language, a common occurrence in some applications. Both cross-language and within-language retrieval are included in this formulation, but it is the cross-language aspect of the problem which distinguishes multilingual text retrieval from its well studied monolingual counterpart. At the SIGIR 96 workshop on "Cross-Linguistic Information Retrieval" the participants discussed the proliferation of terminology being used to describe the field and settled on "Cross-Language" as the best single description of the salient aspect of the problem. "Multilingual" was felt to be too broad, since that term has also been used to describe systems able to perform within-language retrieval in more than one language but that lack any cross-language capability. "Cross-lingual" and "cross-linguistic" were felt to be equally good descriptions of the field, but "crosslanguage" was selected as the preferred term in the interest of standardization. Unfortunately, at about the same time the U.S. Defense Advanced Research Projects Agency (DARPA) introduced "translingual" as their preferred term, so we are still some distance from reaching consensus on this matter.
  16. Smith, D.A.; Shadbolt, N.R.: FacetOntology : expressive descriptions of facets in the Semantic Web (2012) 0.00 
    2.3255666E-4 = product of:
  0.0034883497 = sum of:
    0.0034883497 = product of:
      0.0069766995 = sum of:
        0.0069766995 = weight(_text_:information in 2208) [ClassicSimilarity], result of:
          0.0069766995 = score(doc=2208,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.13714671 = fieldWeight in 2208, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2208)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    The formal structure of the information on the Semantic Web lends itself to faceted browsing, an information retrieval method where users can filter results based on the values of properties ("facets"). Numerous faceted browsers have been created to browse RDF and Linked Data, but these systems use their own ontologies for defining how data is queried to populate their facets. Since the source data is the same format across these systems (specifically, RDF), we can unify the different methods of describing how to quer the underlying data, to enable compatibility across systems, and provide an extensible base ontology for future systems. To this end, we present FacetOntology, an ontology that defines how to query data to form a faceted browser, and a number of transformations and filters that can be applied to data before it is shown to users. FacetOntology overcomes limitations in the expressivity of existing work, by enabling the full expressivity of SPARQL when selecting data for facets. By applying a FacetOntology definition to data, a set of facets are specified, each with queries and filters to source RDF data, which enables faceted browsing systems to be created using that RDF data.
  17. Wang, Y.-H.; Jhuo, P.-S.: ¬A semantic faceted search with rule-based inference (2009) 0.00 
    1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 540) [ClassicSimilarity], result of:
          0.005919926 = score(doc=540,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 540, 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=540)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    Semantic Search has become an active research of Semantic Web in recent years. The classification methodology plays a pretty critical role in the beginning of search process to disambiguate irrelevant information. However, the applications related to Folksonomy suffer from many obstacles. This study attempts to eliminate the problems resulted from Folksonomy using existing semantic technology. We also focus on how to effectively integrate heterogeneous ontologies over the Internet to acquire the integrity of domain knowledge. A faceted logic layer is abstracted in order to strengthen category framework and organize existing available ontologies according to a series of steps based on the methodology of faceted classification and ontology construction. The result showed that our approach can facilitate the integration of inconsistent or even heterogeneous ontologies. This paper also generalizes the principles of picking appropriate facets with which our facet browser completely complies so that better semantic search result can be obtained.
  18. Surfing versus Drilling for knowledge in science : When should you use your computer? When should you use your brain? (2018) 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 4564) [ClassicSimilarity], result of:
          0.0055813594 = score(doc=4564,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.10971737 = fieldWeight in 4564, 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=4564)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    For this second Special Issue of Infozine, we have invited students, teachers, researchers, and software developers to share their opinions about one or the other aspect of this broad topic: how to balance drilling (for depth) vs. surfing (for breadth) in scientific learning, teaching, research, and software design - and how the modern digital-liberal system affects our ability to strike this balance. This special issue is meant to provide a wide and unbiased spectrum of possible viewpoints on the topic, helping readers to define lucidly their own position and information use behavior.
    Content
    Editorial: Surfing versus Drilling for Knowledge in Science: When should you use your computer? When should you use your brain? Blaise Pascal: Les deux infinis - The two infinities / Philippe Hünenberger and Oliver Renn - "Surfing" vs. "drilling" in the modern scientific world / Antonio Loprieno - Of millimeter paper and machine learning / Philippe Hünenberger - From one to many, from breadth to depth - industrializing research / Janne Soetbeer - "Deep drilling" requires "surfing" / Gerd Folkers and Laura Folkers - Surfing vs. drilling in science: A delicate balance / Alzbeta Kubincová - Digital trends in academia - for the sake of critical thinking or comfort? / Leif-Thore Deck - I diagnose, therefore I am a Doctor? Will drilling computer software replace human doctors in the future? / Yi Zheng - Surfing versus drilling in fundamental research / Wilfred van Gunsteren - Using brain vs. brute force in computational studies of biological systems / Arieh Warshel - Laboratory literature boards in the digital age / Jeffrey Bode - Research strategies in computational chemistry / Sereina Riniker - Surfing on the hype waves or drilling deep for knowledge? A perspective from industry / Nadine Schneider and Nikolaus Stiefl - The use and purpose of articles and scientists / Philip Mark Lund - Can you look at papers like artwork? / Oliver Renn - Dynamite fishing in the data swamp / Frank Perabo 34 Streetlights, augmented intelligence, and information discovery / Jeffrey Saffer and Vicki Burnett - "Yes Dave. Happy to do that for you." Why AI, machine learning, and blockchain will lead to deeper "drilling" / Michiel Kolman and Sjors de Heuvel - Trends in scientific document search ( Stefan Geißler - Power tools for text mining / Jane Reed 42 Publishing and patenting: Navigating the differences to ensure search success / Paul Peters
  19. Schaefer, A.; Jordan, M.; Klas, C.-P.; Fuhr, N.: Active support for query formulation in virtual digital libraries : a case study with DAFFODIL (2005) 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 4296) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=4296,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 4296, 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=4296)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    Daffodil is a front-end to federated, heterogeneous digital libraries targeting at strategic support of users during the information seeking process. This is done by offering a variety of functions for searching, exploring and managing digital library objects. However, the distributed search increases response time and the conceptual model of the underlying search processes is inherently weaker. This makes query formulation harder and the resulting waiting times can be frustrating. In this paper, we investigate the concept of proactive support during the user's query formulation. For improving user efficiency and satisfaction, we implemented annotations, proactive support and error markers on the query form itself. These functions decrease the probability for syntactical or semantical errors in queries. Furthermore, the user is able to make better tactical decisions and feels more confident that the system handles the query properly. Evaluations with 30 subjects showed that user satisfaction is improved, whereas no conclusive results were received for efficiency.
  20. 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.

Authors

Years

Languages

  • e 15
  • d 5

Types

Themes