Query construction for user-guided knowledge discovery in databases

Zhengxin Chen, Qiuming Zhu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Knowledge discovery in databases (KDD) and data mining have good potential in many applications. However, in order to make KDD useful, many problems remain to be solved. One such problem is the query formulation problem: "What to do if one does not know how to specify the desired query to begin with?" In this paper we explore an approach to deal with this problem. We describe a conceptual model for user-guided knowledge discovery, and a methodology for query construction based on this model. The methodology allows the user to express what kind of knowledge is to be discovered, thus incorporating user intention to alleviate the overabundance problem which has hampered the development of data mining. A user starts from the goal at the top-most level, and refines queries under the guide of an incrementally constructed causal network. The process of query construction is illustrated by examples.

Original languageEnglish (US)
Pages (from-to)49-64
Number of pages16
JournalInformation Sciences
Issue number1-4
StatePublished - Aug 1998


  • Causal network
  • Conceptual query processing
  • Data mining
  • Knowledge discovery in databases (KDD)
  • User-guided query construction

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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