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  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Otto, A.: Ordnungssysteme als Wissensbasis für die Suche in textbasierten Datenbeständen : dargestellt am Beispiel einer soziologischen Bibliographie (1998) 0.01 
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    Abstract
    Es wird eine Methode vorgestellt, wie sich Ordnungssysteme für die Suche in textbasierten Datenbeständen verwenden lassen. "Ordnungssystem" wird hier als Oberbegriff für beliebige geordnete Begriffssammlungen verwendet. Dies sind beispielsweise Thesauri, Klassifikationen und formale Systematiken. Weil Thesauri dabei die leistungsfähigsten Ordnungssysteme sind, finden sie eine besondere Berücksichtigung. Der Beitrag ist streng praxisbezogenen und auf die Nutzerschnittstelle konzentriert. Die Basis für die Nutzerschnittstelle bilden Ordnungssysteme, die über eine WWW-Schnittstelle angeboten werden. Je nach Fachgebiet kann der Nutzer ein spezielles Ordnungssystem für die Suche auswählen. Im Unterschied zu klassischen Verfahren werden die Ordnungssysteme nicht zur ausschließlichen Suche in Deskriptorenfeldern, sondern für die Suche in einem Basic Index verwendet. In der Anwendung auf den Basic Index sind die Ordnungssysteme quasi "entkoppelt" von der ursprünglichen Datenbank und den Deskriptorenfeldern, für die das Ordnungssystem entwickelt wurde. Die Inhalte einer Datenbank spielen bei der Wahl der Ordnungssysteme zunächst keine Rolle. Sie machen sich erst bei der Suche in der Anzahl der Treffer bemerkbar: so findet ein rechtswissenschaftlicher Thesaurus natürlicherweise in einer Medizin-Datenbank weniger relevante Dokumente als in einer Rechts-Datenbank, weil das im Rechts-Thesaurus abgebildete Begriffsgut eher in einer Rechts-Datenbank zu finden ist. Das Verfahren ist modular aufgebaut und sieht in der Konzeption nachgeordnete semantische Retrievalverfahren vor, die zu einer Verbesserung von Retrievaleffektivität und -effizienz führen werden. So werden aus einer Ergebnismenge, die ausschließlich durch exakten Zeichenkettenabgleich gefunden wurde, in einem nachfolgenden Schritt durch eine semantische Analyse diejenigen Dokumente herausgefiltert, die für die Suchfrage relevant sind. Die WWW-Nutzerschnittstelle und die Verwendung bereits bestehender Ordnungssysteme führen zu einer Minimierung des Arbeitsaufwands auf Nutzerseite. Die Kosten für eine Suche lassen sich sowohl auf der Input-Seite verringern, indem eine aufwendige "manuelle" Indexierung entfällt, als auch auf der Output-Seite, indem den Nutzern leicht bedienbare Suchoptionen zur Verfügung gestellt werden
  2. Kwok, K.L.: ¬A network approach to probabilistic information retrieval (1995) 0.01 
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    Abstract
    Shows how probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network. The network supports adaptation of connection weights as well as the growing of new edges between queries and terms based on user relevance feedback data for training, and it reflects query modification and expansion in information retrieval. A learning rule is applied that can also be viewed as supporting sequential learning using a harmonic sequence learning rate. Experimental results with 4 standard small collections and a large Wall Street Journal collection show that small query expansion levels of about 30 terms can achieve most of the gains at the low-recall high-precision region, while larger expansion levels continue to provide gains at the high-recall low-precision region of a precision recall curve
  3. Schwartz, C.: Web search engines (1998) 0.01 
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    Source
    Journal of the American Society for Information Science. 49(1998) no.11, S.973-982
  4. Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998) 0.01 
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    Source
    Journal of information science. 24(1998) no.1, S.3-18.
  5. Greenberg, J.: Optimal query expansion (QE) processing methods with semantically encoded structured thesaurus terminology (2001) 0.01 
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    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.6, S.487-498
  6. Nie, J.-Y.: Query expansion and query translation as logical inference (2003) 0.01 
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    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.4, S.335-346
  7. Khan, M.S.; Khor, S.: Enhanced Web document retrieval using automatic query expansion (2004) 0.01 
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    Source
    Journal of the American Society for Information Science and technology. 55(2004) no.1, S.29-40
  8. Scholer, F.; Williams, H.E.; Turpin, A.: Query association surrogates for Web search (2004) 0.01 
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    Source
    Journal of the American Society for Information Science and technology. 55(2004) no.7, S.637-650
  9. Menczer, F.: Lexical and semantic clustering by Web links (2004) 0.01 
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    Journal of the American Society for Information Science and Technology. 55(2004) no.14, S.1261-1269
  10. Pahlevi, S.M.; Kitagawa, H.: Conveying taxonomy context for topic-focused Web search (2005) 0.01 
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    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.2, S.173-188
  11. Nakashima, M.; Sato, K.; Qu, Y.; Ito, T.: Browsing-based conceptual information retrieval incorporating dictionary term relations, keyword associations, and a user's interest (2003) 0.01 
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    Journal of the American Society for Information Science and technology. 54(2003) no.1, S.16-28
  12. Blocks, D.; Cunliffe, D.; Tudhope, D.: ¬A reference model for user-system interaction in thesaurus-based searching (2006) 0.01 
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    Journal of the American Society for Information Science and Technology. 57(2006) no.12, S.1655-1665
  13. Huang, L.; Milne, D.; Frank, E.; Witten, I.H.: Learning a concept-based document similarity measure (2012) 0.01 
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    Journal of the American Society for Information Science and Technology. 63(2012) no.8, S.1593-1608
  14. Narock, T.; Zhou, L.; Yoon, V.: Semantic similarity of ontology instances using polarity mining (2013) 0.01 
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    Journal of the American Society for Information Science and Technology. 64(2013) no.2, S.416-427
  15. 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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    Journal of the Association for Information Science and Technology. 65(2014) no.5, S.948-963
  16. Järvelin, K.; Niemi, T.: Deductive information retrieval based on classifications (1993) 0.01 
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    Journal of the American Society for Information Science. 44(1993) no.10, S.557-578
  17. Colace, F.; Santo, M. de; Greco, L.; Napoletano, P.: Improving relevance feedback-based query expansion by the use of a weighted word pairs approach (2015) 0.01 
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    Journal of the Association for Information Science and Technology. 66(2015) no.11, S.2223-2234
  18. Sanfilippo, M.; Yang, S.; Fichman, P.: Trolling here, there, and everywhere : perceptions of trolling behaviors in context (2017) 0.01 
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    Journal of the Association for Information Science and Technology. 68(2017) no.10, S.2313-2327
  19. Lee, Y.-Y.; Ke, H.; Yen, T.-Y.; Huang, H.-H.; Chen, H.-H.: Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement (2020) 0.01 
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    Journal of the Association for Information Science and Technology. 71(2020) no.6, S.657-670
  20. Hoeber, O.: ¬A study of visually linked keywords to support exploratory browsing in academic search (2022) 0.01 
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    Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1171-1191

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