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Discovering Robust Knowledge from Databases that Change

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Abstract

Many applications of knowledge discovery and data mining such as rule discovery for semantic query optimization, database integration and decision support, require the knowledge to be consistent with the data. However, databases usually change over time and make machine-discovered knowledge inconsistent. Useful knowledge should be robust against database changes so that it is unlikely to become inconsistent after database updates. This paper defines this notion of robustness in the context of relational databases and describes how robustness of first-order Horn-clause rules can be estimated. Experimental results show that our estimation approach can accurately identify robust rules. We also present a rule antecedent pruning algorithm that improves the robustness and applicability of machine discovered rules to demonstrate the usefulness of robustness estimation.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Arizona State University, PO Box 875406, Tempe, AZ, 85287, USA

    Chun-Nan Hsu

  2. Information Sciences Institute and Department of Computer Science, University of Southern California, 4676, Admiralty Way, Marina del Rey, CA, 90292, USA

    Craig A. Knoblock

Authors
  1. Chun-Nan Hsu
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  2. Craig A. Knoblock
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Hsu, CN., Knoblock, C.A. Discovering Robust Knowledge from Databases that Change. Data Mining and Knowledge Discovery 2, 69–95 (1998). https://doi.org/10.1023/A:1009717820785

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  • DOI: https://doi.org/10.1023/A:1009717820785