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  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Rahmstorf, G.: Integriertes Management inhaltlicher Datenarten (2001) 0.03 
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    Abstract
    Inhaltliche Daten sind im Unterschied zu Messdaten, Zahlen, Analogsignalen und anderen Informationen solche Daten, die sich auch sprachlich interpretieren lassen. Sie transportieren Inhalte, die sich benennen lassen. Zu inhaltlichen Daten gehören z. B. Auftragsdaten, Werbetexte, Produktbezeichnungen und Patentklassifikationen. Die meisten Daten, die im Internet kommuniziert werden, sind inhaltliche Daten. Man kann inhaltliche Daten in vier Klassen einordnen: * Wissensdaten - formatierte Daten (Fakten u. a. Daten in strukturierter Form), - nichtformatierte Daten (vorwiegend Texte); * Zugriffsdaten - Benennungsdaten (Wortschatz, Terminologie, Themen u. a.), - Begriffsdaten (Ordnungs- und Bedeutungsstrukturen). In der Wissensorganisation geht es hauptsächlich darum, die unüberschaubare Fülle des Wissens zu ordnen und wiederauffindbar zu machen. Daher befasst sich das Fach nicht nur mit dem Wissen selbst, selbst sondern auch mit den Mitteln, die dazu verwendet werden, das Wissen zu ordnen und auffindbar zu machen
    Language
    d
  2. Hauer, M.: Neue OPACs braucht das Land ... dandelon.com (2006) 0.03 
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    Abstract
    In dandelon.com werden im Gegensatz zu den bisherigen Federated Search-Portal-Ansätzen die Titel von Medien neu mittels intelligentCAPTURE dezentral und kollaborativ erschlossen und inhaltlich stark erweitert. intelligentCAPTURE erschließt maschinell bisher Buchinhaltsverzeichnisse, Bücher, Klappentexte, Aufsätze und Websites, übernimmt bibliografische Daten aus Bibliotheken (XML, Z.39.50), von Verlagen (ONIX + Cover Pages), Zeitschriftenagenturen (Swets) und Buchhandel (SOAP) und exportierte maschinelle Indexate und aufbereitete Dokumente an die Bibliothekskataloge (MAB, MARC, XML) oder Dokumentationssysteme, an dandelon.com und teils auch an Fachportale. Die Daten werden durch Scanning und OCR, durch Import von Dateien und Lookup auf Server und durch Web-Spidering/-Crawling gewonnen. Die Qualität der Suche in dandelon.com ist deutlich besser als in bisherigen Bibliothekssystemen. Die semantische, multilinguale Suche mit derzeit 1,2 Millionen Fachbegriffen trägt zu den guten Suchergebnissen stark bei.
    Language
    d
  3. Schek, M.: Automatische Klassifizierung in Erschließung und Recherche eines Pressearchivs (2006) 0.02 
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    Abstract
    Die Süddeutsche Zeitung (SZ) verfügt seit ihrer Gründung 1945 über ein Pressearchiv, das die Texte der eigenen Redakteure und zahlreicher nationaler und internationaler Publikationen dokumentiert und für Recherchezwecke bereitstellt. Die DIZ-Pressedatenbank (www.medienport.de) ermöglicht die browserbasierte Recherche für Redakteure und externe Kunden im Intra- und Internet und die kundenspezifischen Content Feeds für Verlage, Rundfunkanstalten und Portale. Die DIZ-Pressedatenbank enthält z. Zt. 7,8 Millionen Artikel, die jeweils als HTML oder PDF abrufbar sind. Täglich kommen ca. 3.500 Artikel hinzu, von denen ca. 1.000 durch Dokumentare inhaltlich erschlossen werden. Die Informationserschließung erfolgt im DIZ nicht durch die Vergabe von Schlagwörtern am Dokument, sondern durch die Verlinkung der Artikel mit "virtuellen Mappen", den Dossiers. Insgesamt enthält die DIZ-Pressedatenbank ca. 90.000 Dossiers, die untereinander zum "DIZ-Wissensnetz" verlinkt sind. DIZ definiert das Wissensnetz als Alleinstellungsmerkmal und wendet beträchtliche personelle Ressourcen für die Aktualisierung und Qualitätssicherung der Dossiers auf. Im Zuge der Medienkrise mussten sich DIZ der Herausforderung stellen, bei sinkenden Lektoratskapazitäten die Qualität der Informationserschließung im Input zu erhalten. Auf der Outputseite gilt es, eine anspruchsvolle Zielgruppe - u.a. die Redakteure der Süddeutschen Zeitung - passgenau und zeitnah mit den Informationen zu versorgen, die sie für ihre tägliche Arbeit benötigt. Bezogen auf die Ausgangssituation in der Dokumentation der Süddeutschen Zeitung identifizierte DIZ drei Ansatzpunkte, wie die Aufwände auf der Inputseite (Lektorat) zu optimieren sind und gleichzeitig auf der Outputseite (Recherche) das Wissensnetz besser zu vermarkten ist: - (Teil-)Automatische Klassifizierung von Pressetexten (Vorschlagwesen) - Visualisierung des Wissensnetzes - Neue Retrievalmöglichkeiten (Ähnlichkeitssuche, Clustering) Im Bereich "Visualisierung" setzt DIZ auf den Net-Navigator von intelligent views, eine interaktive Visualisierung allgemeiner Graphen, basierend auf einem physikalischen Modell. In den Bereichen automatische Klassifizierung, Ähnlichkeitssuche und Clustering hat DIZ sich für das Produkt nextBot der Firma Brainbot entschieden.
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    d
    Location
    D
  4. Faaborg, A.; Lagoze, C.: Semantic browsing (2003) 0.02 
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    d
    Source
    Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003
  5. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.02 
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    Date
    11. 2.2011 18:22:58
    Language
    d
  6. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.02 
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    Date
    11. 2.2011 18:22:25
    Language
    d
  7. Lund, K.; Burgess, C.; Atchley, R.A.: Semantic and associative priming in high-dimensional semantic space (1995) 0.02 
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    Source
    Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society: July 22 - 25, 1995, University of Pittsburgh / ed. by Johanna D. Moore and Jill Fain Lehmann
  8. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.01 
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
  9. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.01 
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    Date
    30. 3.2001 13:32:22
  10. Wang, Z.; Khoo, C.S.G.; Chaudhry, A.S.: Evaluation of the navigation effectiveness of an organizational taxonomy built on a general classification scheme and domain thesauri (2014) 0.01 
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  11. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.01 
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    Date
    22. 3.2003 19:35:46
  12. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.01 
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    Abstract
    The study reported here investigated the query expansion behavior of end-users interacting with a thesaurus-enhanced search system on the Web. Two groups, namely academic staff and postgraduate students, were recruited into this study. Data were collected from 90 searches performed by 30 users using the OVID interface to the CAB abstracts database. Data-gathering techniques included questionnaires, screen capturing software, and interviews. The results presented here relate to issues of search-topic and search-term characteristics, number and types of expanded queries, usefulness of thesaurus terms, and behavioral differences between academic staff and postgraduate students in their interaction. The key conclusions drawn were that (a) academic staff chose more narrow and synonymous terms than did postgraduate students, who generally selected broader and related terms; (b) topic complexity affected users' interaction with the thesaurus in that complex topics required more query expansion and search term selection; (c) users' prior topic-search experience appeared to have a significant effect on their selection and evaluation of thesaurus terms; (d) in 50% of the searches where additional terms were suggested from the thesaurus, users stated that they had not been aware of the terms at the beginning of the search; this observation was particularly noticeable in the case of postgraduate students.
    Date
    22. 7.2006 16:32:43
  13. Gillitzer, B.: Yewno (2017) 0.01 
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    Date
    22. 2.2017 10:16:49
    Language
    d
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Authors

Years

Languages

  • e 54
  • d 40
  • f 1
  • More… Less…

Types

  • a 83
  • el 14
  • m 4
  • r 4
  • x 2
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