Twofold fuzzy sets and rough sets—Some issues in knowledge representation
Fuzzy Sets and Systems
The EGG/YOLK reliability hierarchy: semantic data integration using sorts with prototypes
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
From the Egg-Yolk to the Scrambled-Egg Theory
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Fuzzy Modeling with Spatial Information for Geographic Problems
Fuzzy Modeling with Spatial Information for Geographic Problems
Spatio-Temporal Databases: Flexible Querying and Reasoning
Spatio-Temporal Databases: Flexible Querying and Reasoning
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In the past, theories of quality measuring of imperfect data have always assumed the use of perfect reference data. In practice, this isn't always the case. In our new approach we consider the possibility of measuring quality using imperfect reference data. Especially the use of classified satellite images is considered. The question we want to answer is what can be said about the quality of a questioned object taking the quality of the reference data as input: is it good, is it bad or is it unknown? In first instance, an imperfection model for the reference data is built based on possibility theory. From this we extract a fuzzy egg-yolk model, consisting of two parts: a fuzzy set of what must be in the object and a fuzzy set of what might be in the object. A special buffering operation is defined to build in tolerance in relation to the mistakes in the classified satellite images. Operators are defined for measuring quality using this model. Finally the method is tested and evaluated. It is shown that bad quality can often be stated, but that it is difficult to confirm good quality when the quality of the reference data is insufficient.