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  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Brezillon, P.; Saker, I.: Modeling context in information seeking (1999) 0.00 
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    Pages
    S.479-493
  2. Jun, W.: ¬A knowledge network constructed by integrating classification, thesaurus and metadata in a digital library (2003) 0.00 
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    Source
    Bulletin of the American Society for Information Science. 29(2003) no.2, S.24-28
  3. Bilal, D.; Kirby, J.: Differences and similarities in information seeking : children and adults as Web users (2002) 0.00 
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    Source
    Information processing and management. 38(2002) no.5, S.649-670
  4. Imhof, A.: RSWK/SWD und Faceted Browsing : neue Möglichkeiten einer inhaltlich-intuitiven Navigation (2006) 0.00 
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    Bibliotheksdienst. 40(2006) H.8/9, S.1015-1025
  5. Schek, M.: Automatische Klassifizierung in Erschließung und Recherche eines Pressearchivs (2006) 0.00 
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    Pages
    S.313-324
  6. Tudhope, D.; Binding, C.; Blocks, D.; Cunliffe, D.: FACET: thesaurus retrieval with semantic term expansion (2002) 0.00 
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    Pages
    S.384
  7. Shah, C.: Collaborative information seeking : the art and science of making the whole greater than the sum of all (2012) 0.00 
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    Pages
    XXI, 185 S
  8. Semantic search over the Web (2012) 0.00 
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    Pages
    XX, 423 S
  9. Koopman, B.; Zuccon, G.; Bruza, P.; Sitbon, L.; Lawley, M.: Information retrieval as semantic inference : a graph Inference model applied to medical search (2016) 0.00 
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    Source
    Information Retrieval Journal. 19(2016) no.1, S.6-37
  10. Layfield, C.; Azzopardi, J,; Staff, C.: Experiments with document retrieval from small text collections using Latent Semantic Analysis or term similarity with query coordination and automatic relevance feedback (2017) 0.00 
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    Pages
    S.25-36
  11. Vo, D.-T.; Bagheri, E.: Feature-enriched matrix factorization for relation extraction (2019) 0.00 
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    Source
    Information processing and management. 56(2019) no.3, S.424-444
  12. Sebastian, Y.: Literature-based discovery by learning heterogeneous bibliographic information networks (2017) 0.00 
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    Pages
    xix, 215 S
  13. Ingwersen, P.; Järvelin, K.: ¬The turn : integration of information seeking and retrieval in context (2005) 0.00 
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    Footnote
    Rez. in: Mitt. VÖB 59(2006) H.2, S.81-83 (O. Oberhauser): "Mit diesem Band haben zwei herausragende Vertreter der europäischen Informationswissenschaft, die Professoren Peter Ingwersen (Kopenhagen) und Kalervo Järvelin (Tampere) ein Werk vorgelegt, das man vielleicht dereinst als ihr opus magnum bezeichnen wird. Mich würde dies nicht überraschen, denn die Autoren unternehmen hier den ambitionierten Versuch, zwei informations wissenschaftliche Forschungstraditionen, die einander bisher in eher geringem Ausmass begegneten, unter einem gesamtheitlichen kognitiven Ansatz zu vereinen - das primär im sozialwissenschaftlichen Bereich verankerte Forschungsgebiet "Information Seeking and Retrieval" (IS&R) und das vorwiegend im Informatikbereich angesiedelte "Information Retrieval" (IR). Dabei geht es ihnen auch darum, den seit etlichen Jahren zwar dominierenden, aber auch als zu individualistisch kritisierten kognitiven Ansatz so zu erweitern, dass technologische, verhaltensbezogene und kooperative Aspekte in kohärenter Weise berücksichtigt werden. Dies geschieht auf folgende Weise in neun Kapiteln: - Zunächst werden die beiden "Lager" - die an Systemen und Laborexperimenten orientierte IR-Tradition und die an Benutzerfragen orientierte IS&R-Fraktion - einander gegenübergestellt und einige zentrale Begriffe geklärt. - Im zweiten Kapitel erfolgt eine ausführliche Darstellung der kognitiven Richtung der Informationswissenschaft, insbesondere hinsichtlich des Informationsbegriffes. - Daran schliesst sich ein Überblick über die bisherige Forschung zu "Information Seeking" (IS) - eine äusserst brauchbare Einführung in die Forschungsfragen und Modelle, die Forschungsmethodik sowie die in diesem Bereich offenen Fragen, z.B. die aufgrund der einseitigen Ausrichtung des Blickwinkels auf den Benutzer mangelnde Betrachtung der Benutzer-System-Interaktion. - In analoger Weise wird im vierten Kapitel die systemorientierte IRForschung in einem konzentrierten Überblick vorgestellt, in dem es sowohl um das "Labormodell" als auch Ansätze wie die Verarbeitung natürlicher Sprache und Expertensysteme geht. Aspekte wie Relevanz, Anfragemodifikation und Performanzmessung werden ebenso angesprochen wie die Methodik - von den ersten Laborexperimenten bis zu TREC und darüber hinaus.
    Pages
    XIV, 448 S
  14. Schek, M.: Automatische Klassifizierung und Visualisierung im Archiv der Süddeutschen Zeitung (2005) 0.00 
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    Medienwirtschaft. 2(2005) H.1, S.20-24
  15. Hannech, A.: Système de recherche d'information étendue basé sur une projection multi-espaces (2018) 0.00 
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    Abstract
    Since its appearance in the early 90's, the World Wide Web (WWW or Web) has provided universal access to knowledge and the world of information has been primarily witness to a great revolution (the digital revolution). It quickly became very popular, making it the largest and most comprehensive database and knowledge base thanks to the amount and diversity of data it contains. However, the considerable increase and evolution of these data raises important problems for users, in particular for accessing the documents most relevant to their search queries. In order to cope with this exponential explosion of data volume and facilitate their access by users, various models are offered by information retrieval systems (IRS) for the representation and retrieval of web documents. Traditional SRIs use simple keywords that are not semantically linked to index and retrieve these documents. This creates limitations in terms of the relevance and ease of exploration of results. To overcome these limitations, existing techniques enrich documents by integrating external keywords from different sources. However, these systems still suffer from limitations that are related to the exploitation techniques of these sources of enrichment. When the different sources are used so that they cannot be distinguished by the system, this limits the flexibility of the exploration models that can be applied to the results returned by this system. Users then feel lost to these results, and find themselves forced to filter them manually to select the relevant information. If they want to go further, they must reformulate and target their search queries even more until they reach the documents that best meet their expectations. In this way, even if the systems manage to find more relevant results, their presentation remains problematic. In order to target research to more user-specific information needs and improve the relevance and exploration of its research findings, advanced SRIs adopt different data personalization techniques that assume that current research of user is directly related to his profile and / or previous browsing / search experiences.
    Pages
    351 S
  16. Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.00 
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    Source
    D-Lib magazine. 8(2002) no.10, x S

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