Functional dependencies in relations with null values
Information Processing Letters
Knowledge acquisition by methods of formal concept analysis
Proceedings of the conference on Data analysis, learning symbolic and numeric knowledge
Attribute exploration with background knowledge
Theoretical Computer Science
On the Equivalence of Database Models
Journal of the ACM (JACM)
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
On the Treatment of Incomplete Knowledge in Formal Concept Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Theory of Relational Databases
Theory of Relational Databases
Usability Issues in Description Logic Knowledge Base Completion
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Treating incomplete knowledge in formal concept analysis
Formal Concept Analysis
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Formal contexts with unknown entries can be represented by three-valued contexts K=(G, M, {×, o, ?}, I), where a question mark indicates that it is not known whether the object g∈G has the attribute m∈M. To describe logical formulas between columns of such incomplete contexts the Kripke-semantics are used for propositional formulas over the set M of attributes. Attribute implications are considered as special propositional formulas. If a context is too large to be fully represented, an interactive computer algorithm may help the user to get maximal information (with respect to his knowledge) about the valid attribute implications of the unknown context. This computer algorithm is called "attribute exploration".