Robust Chun - Nan

Schema: 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 defines 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. geoloc(name,glc~ode,country,latitude,longitude), seaport(name,glc-code,storage,rail,road,anch~ffshore), wharf(id,glc-code,depth,length,crane), ship(name,class,status,fleet,year), ship-class(classaame,type,draft,length,container-cap). Rules: Rl: ;The latitude of a Maltese geographic location is greater ;than or equal to 35.89. ?latitude 1 35.89 e: geoloc( -)-, ?country,?latitude,-) A ?country = “Malta”. R2: ;A11 Maltese geographic locations are seaports. seaport (,?glcrd ,-,-,-,-) e geoloc(,?glc-cd,?country,,,-) A ?country = “Malta”. F?,3: ;A11 ships built in 1981 belong to either “MSC” fleet or *“MSC Lease” fleet.