Query construction for user-guided knowledge discovery in databases

Authors:
Zhengxin Chen;Qiuming Zhu
Affiliations:
Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182-0500, USA;Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182-0500, USA
Venue:
Information Sciences: an International Journal
Year:
1998

Citing 7
Cited 2

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
KDD–93: progress and challenges in knowledge discovery in databases

AI Magazine
A database perspective on knowledge discovery

Communications of the ACM
Advances in knowledge discovery and data mining

Advances in knowledge discovery and data mining
an entropy-driven system for construction of probabilistic expert systems from databases

UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Editorial

Data Mining and Knowledge Discovery

Global data mining: An empirical study of current trends, future forecasts and technology diffusions

Expert Systems with Applications: An International Journal
Man-machine collaboration to acquire cooking adaptation knowledge for the TAAABLE case-based reasoning system

Proceedings of the 21st international conference companion on World Wide Web

Quantified Score

Hi-index	0.07

Visualization

Abstract

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.