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AAAI
1996

Discovering Robust Knowledge from Dynamic Closed World Data

8 years 3 months ago
Discovering Robust Knowledge from Dynamic Closed World Data
Many applications of knowledge discovery require the knowledge to be consistent with data. Examples include discovering rules for query optimization, database integration, decision support, etc. However, databases usually change over time and make machine-discovered knowledge inconsistent with data. 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, describes how to estimate the robustness of Hornclause rules in closed-world databases, and describes how the robustness estimation can be applied in rule discovery systems.
Chun-Nan Hsu, Craig A. Knoblock
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1996
Where AAAI
Authors Chun-Nan Hsu, Craig A. Knoblock
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