Discovering Robust Knowledge from Databases that Change

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 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 changes. This paper de nes this notion of robustness in the context of relational databases that contain multiple relations and describes how robustness of rst-order Horn-clause rules can be estimated and applied in knowledge discovery. Our experiments show that the estimation approach can accurately predict the robustness of a rule.

• Type: Journal
• Year: 1998
• Where: DATAMINE
• Authors: Chun-Nan Hsu, Craig A. Knoblock
• Keywords: Database Changes, Databases, DATAMINE 1998, Knowledge Discovery