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Search in Documents Based on Topical Development

  • Jan Martinovič
  • Václav Snášel
  • Jiří Dvorský
  • Pavla Dráždilová
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 67)

Abstract

An important service for systems providing access to information is the organization of returned search results. Vector model search results may be represented by a sphere in an n-dimensional space. A query represents the center of this sphere whose size is determined by its radius or by the amount of documents it contains. The goal of searching is to have all documents relevant to a query present within this sphere. It is known that not all relevant documents are present in this sphere and that is why various methods for improving search results, which can be implemented on the basis of expanding the original question, have been developed. Our goal is to utilize knowledge of document similarity contained in textual databases to obtain a larger amount of relevant documents while minimizing those cancelled due to their irrelevance. In the article we will define the concept k-path (topical development). For the individual development of vector query results, we will propose the SORT-EACH algorithm, which uses the aforementioned methods for acquiring topical development.

Keywords

Topical Development Clustering Information Retrieval 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jan Martinovič
    • 1
  • Václav Snášel
    • 1
  • Jiří Dvorský
    • 1
  • Pavla Dráždilová
    • 1
  1. 1.Department of Computer ScienceVŠB - Technical University of OstravaOstrava-PorubaCzech Republic

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