1. Criteria for building concepts in CCEC Karina Gibert 1, Alejandra Pérez-Bonilla 1, Darko Vrecko 2 1 Department of Statistics and Operations Research.Technical University of Catalonia. 2 Department of Systems and Control. Jozef Stefan Institute

2. Chaining Concepts: CCEC overview Best Global concept and Close-World AssumptionBest Global concept and Close-World Assumption KB Máx Confidence Máx Relat. Covering KB P(r)=1 r:A 1 → C 1 r:A 2 → C 1 r:A 3 → C 1 CWA RelCov=100% C 1 : A 1 1 ^A 1 2 ^A 1 3 C 2 : not C 1 C 1 : A 1 1 ٧A 1 2 ٧A 1 3 C 2 : not C 1

3. Split by consequent Máx Confidence Máx Relat. Covering KB P(r)=1 r:A 1 → C 1 r:A 2 → C 1 r:A 3 → C 1 RelCov=100% Chaining Concepts: CCEC overview KB C 1 : A 1 1 ^A 1 2 ^A 1 3 C 2 : A 2 1 ٧A 2 2 ٧A 2 3 Best local concept and no Close-World AssumptionBest local concept and no Close-World Assumption Máx Confidence Máx Relat. Covering KB P(r)=1 r:A 1 → C 1 r:A 2 → C 1 r:A 3 → C 1 RelCov=100% C1 C2 C 1 : A 1 1 ٧A 1 2 ٧A 1 3 C 2 : A 2 1 ^ A 2 2 ^ A 2 3 No CWA

4. Split by consequent Máx Confidence Máx Relat. Covering KB P(r)=1 r:A 1 → C 1 r:A 2 → C 1 r:A 3 → C 1 RelCov=100% Chaining Concepts: CCEC overview KB C 1 : A 1 1 ^A 1 2 ^A 1 3 ٧ ̚C 2 C 2 : A 2 1 ٧A 2 2 ٧A 2 3 ٧ ̚C 1 Best local concept and Close-World AssumptionBest local concept and Close-World Assumption Máx Confidence Máx Relat. Covering KB P(r)=1 r:A 1 → C 1 r:A 2 → C 1 r:A 3 → C 1 RelCov=100% C1 C2 C 1 : A 1 1 ٧A 1 2 ٧A 1 3 ٧ ̚C 2 C 2 : A 2 1 ^ A 2 2 ^ A 2 3 ٧ ̚C 1 CWA

5. Split by consequent Máx Confidence Máx Relat. Covering KB P(r)=1 r:A 1 → C 1 r:A 2 → C 1 r:A 3 → C 1 RelCov=100% Chaining Concepts: CCEC overview KB C 1 : A 1 1 ^A 1 2 ^A 1 3 ٧ ̚C 2 C 2 : A 2 1 ٧A 2 2 ٧A 2 3 ٧ ̚C 1 Best local concept and partial Close-World AssumptionBest local concept and partial Close-World Assumption Máx Confidence Máx Relat. Covering KB P(r)=1 r:A 1 → C 1 r:A 2 → C 1 r:A 3 → C 1 RelCov=100% C1 C2 C 1 : A 1 1 ٧A 1 2 ٧A 1 3 ٧ ̚C 2 C 2 : A 2 1 ^ A 2 2 ^ A 2 3 ٧ ̚C 1 Partial CWA Only for variables not included in C 1 Only for variables not included in C 2

6. Split by consequent Máx Confidence Máx Relat. Covering KB P(r)=1 r:A 1 → C 1 r:A 2 → C 1 r:A 3 → C 1 RelCov=100% Chaining Concepts: CCEC overview KB C 1 : A 1 1 ^A 1 2 ^A 1 3 ٧ ̚C 2 C 2 : A 2 1 ٧A 2 2 ٧A 2 3 ٧ ̚C 1 Best local-global concept and partial Close-World AssumptionBest local-global concept and partial Close-World Assumption Máx Confidence Máx Relat. Covering KB P(r)=1 r:A 1 → C 1 r:A 2 → C 1 r:A 3 → C 1 RelCov=100% C1 C2 C 1 : A 1 1 ٧A 1 2 ٧A 1 3 ٧ ̚C 2 C 2 : A 2 1 ^ A 2 2 ^ A 2 3 ٧ ̚C 1 Partial CWA Only for variables not included in C 1 Only for variables not included in C 2 Global strategy for repeated variables: Substitute by A 1 2 when higher relCov

7. Is there any question?... Automatic generation of conceptual descriptions of classifications in Environmental Domains International Congress on Environmental Modelling and Software iEMSs 2008 July 7-10, 2008 - Barcelona, Catalonia K. Gibert 1, A. Pérez-Bonilla 1, D. Vrecko 2 1 Department of Statistics and Operations Research.Technical University of Catalonia. 2 Department of Systems and Control. Jozef Stefan Institute