1. Solving inconsistent ontologies with heuristics Joey Lam, Derek Sleeman, Wamberto Vasconcelos 23 Jan 2006

2. Overview Motivation Heuristic Approach Basic Heuristic General Heuristic Individual Heuristic Ranking solution Conclusion and Future Work

3. Motivation Existing ontology editors Detect the inconsistencies Indicate the inconsistent concepts Pinpoint the problematic axioms No functionalities are provided for users to correct the problems.

4. A Heuristic Approach Provide optimal ways for users to correct the detected inconsistencies using a heuristic approach Three types of heuristics: Basic – common errors made by humans General – heuristics made by ontology engineers Individual – heuristics used by a specific user when working on a particular ontology

5. Related Work OntoTrack – provides on-demand explanation of subsumption between classes Protégé-OWL (a) tracks down the reasons for inconsistencies in OWL classes (b) explains the reasons for unsatisfiable OWL classes SWOOP (a) pinpoints the problematic axioms (b) distinguishes root from derived unsatisfiable classes by detecting their dependencies Summary: focus on the explanations of unsatisfiability, but none of the work supports correcting the inconsistency.

6. Make use of the common errors made by humans to generate a set of heuristics For example: Asserted: C1 C2 Meant: C1 C2 Reason: Misunderstanding different between primitive and defined classes Suggest the meant axiom to the user as a replacement Basic Heuristics

7. General Heuristic Conduct an empirical study to identify the strategies used by ontology users when facing inconsistent ontologies What changes are needed with reasons Why not other options Adopt the think-aloud protocol elicitation Record and analyse the verbal reports produced by the subjects

8. Individual Heuristic Individual users may have different perspectives on a problem depending on their knowledge backgrounds. Support personalised repairing strategies Provide individual options by making use of past behaviors to predict future behaviors.

9. Ranking Solutions Rank the solutions based on the degree of confidence Evaluate the cost and benefits of the heuristics Cost: the number of operations required to implement it Benefit: the number of inconsistent and consistent entities resulted by a heuristic

10. Conclusion and Future Work Goal: recommend optimal solutions using a heuristic approach Future work: (a) Investigate the set of heuristics from different sources (b) Optimise and evaluate the proposed solutions