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Identifying Quality Knowledge

  • Shichao Zhang
  • Chengqi Zhang
  • Xindong Wu
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

With the advance of Web techniques, individuals and organizations can make use of low-cost information and knowledge on the Internet when carrying out data mining for applications. However, information from different datasources is often untrustworthy, contradictory, fraudulent, and even potentially dangerous to applications. Therefore, the discovery of reliable knowledge from different data-sources (databases or datasets) has become a critical task in multi-database mining research. In this chapter, a data-source is taken as a knowledge base (From our local pattern analysis, this assumption is reasonable.). A framework is thus presented for identifying quality knowledge from different data-sources.

Keywords

Knowledge Discovery Frequent Itemsets External Knowledge Mining Task Kripke Structure 
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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Notes

  1. 1.
    For convenience, we also call a company a data-source in a knowledge sharing environment. This is because a company is taken as a data-source when the company’s knowledge is also shared by other companies.Google Scholar
  2. 2.
    The consistency is dealt with in Chapter 6. Therefore, we assume that the knowledge in data-sources is consistent for the time being.Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Shichao Zhang
    • 1
  • Chengqi Zhang
    • 1
  • Xindong Wu
    • 2
  1. 1.FIT, Universityof Technology SydneyAustralia
  2. 2.Department of Computer ScienceUniversity of VermontUSA

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