Architecture and Retrieval Methods of a Search Assistant for Scientific Libraries

  • I. Glöckner
  • A. Knoll
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


In this paper, we present the design and retrieval methodology of an intuitively operated retrieval assistant (RA) which supports the thematic search in databases of scientific libraries. The retrieval assistant establishes innovative and more adequate means for expressing a user’s search interest by adopting aggregation operators of natural language (e.g. almost all, as many as possible), the interpretation of which is accomplished by novel methods from fuzzy set theory. These operators can be used in their intuitive meaning, i.e. just as in everyday language, for aggregating over sets of weighted search terms. The required scalability of the system is ensured through its multi-tier architecture, which disburdens both the clients and the external database servers by introducing an (arbitrarily replicable) intermediary to perform the computationally intensive aggregation step.


Aggregation Operator Scientific Library Retrieval Quality Fuzzy Aggregation Meta Search Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Barwise, J. and Cooper, R. (1981): Generalized Quantifiers and Natural Language. Linguistics and Philosophy, 4, 159–219.CrossRefGoogle Scholar
  2. Bordogna, G. and Pasi, G. (1997): A Fuzzy Information Retrieval System Handling Users’ Preferences on Document Sections. In: Dubois, D. and Prade, H. and Yager, R.R. (Eds.), Fuzzy Information Engineering. Wiley, 265–281.Google Scholar
  3. Buell, D.A. (1982): An Analysis of Some Fuzzy Subset Applications to Information Retrieval Systems. Fuzzy Sets and Systems, 7, 35–42.CrossRefGoogle Scholar
  4. Cater, S.C. and Kraft, D.H. (1987): TIRS: A Topological Information System Satisfying the Requirements of the Waller-Kraft Wish List. 10th ACM-SIGIR Conf. on Research and Development in Information Retrieval, 171–180.Google Scholar
  5. FÜhles-Ubach, S. (1997): Analysen zur Unschärfe in Datenbank-und Retrievalsystemen. Doctorial Dissertation, Humboldt-Universität, Berlin.Google Scholar
  6. GlÖCkner, I. (1997): DFS — An Axiomatic Approach to Fuzzy Quantification. Technical Report TR97-06, Technische Fakultät, Universität Bielefeld.Google Scholar
  7. GlÖCkner, I. (1999): A Framework for Evaluating Approaches to Fuzzy Quan-tification. Technical Report TR99-03, Technische Fakultät der Universität Bielefeld.Google Scholar
  8. GlÖckner, I., Knoll, A., and Wolfram, A. (1998): Data Fusion based on Fuzzy Quantifiers. In: Proceedings of EuroFusion’ 98, Great Malvern, UK.Google Scholar
  9. Pfeifer, U. and Fuhr, N. (1995): Efficient Processing of Vague Queries using a Data Stream Approach. Proc. of the 18th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, ACM, New York, 189–198.Google Scholar
  10. Waller, W.G. and Kraft, D.H. (1979): A Mathematical Model of a Weighted Boolean Retrieval System. Information Processing & Management, 15, 235–245.CrossRefGoogle Scholar
  11. Wiederhold, G. (1992): Mediators in the Architecture of Future Information Systems. IEEE Computer, 25(3), 38–49.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • I. Glöckner
    • 1
  • A. Knoll
    • 1
  1. 1.Technische Fakultät, Arbeitsgruppe Technische InformatikUniversität BielefeldBielefeldGermany

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