While DISPONTE semantics offers a novel approach to reasoning with inconsistent knowledge bases by incorporating probabilistic reasoning, it has several limitations compared to repair semantics:
Complexity of Probabilistic Reasoning: The computation of probabilities in DISPONTE can be computationally intensive, especially as the number of probabilistic axioms increases. This complexity can lead to performance issues when dealing with large knowledge bases, making it less efficient than some repair semantics that focus on identifying consistent subsets of axioms.
Limited Expressiveness: DISPONTE semantics primarily focuses on the degree of belief in axioms rather than the structural relationships between them. In contrast, repair semantics explicitly defines repairs as consistent subsets of axioms, allowing for a more structured approach to managing inconsistencies. This can make repair semantics more expressive in certain contexts.
Handling of Inconsistencies: DISPONTE semantics may struggle to provide clear guidance on how to resolve inconsistencies, as it does not inherently suggest which axioms should be removed or modified. Repair semantics, on the other hand, directly addresses this issue by generating repairs that can be used to restore consistency, providing a more actionable framework for knowledge base maintenance.
Interpretation of Probabilities: The interpretation of probabilities in DISPONTE can be subjective, as it relies on the assignment of probability values to axioms. This can lead to inconsistencies in how different users or systems interpret the same knowledge base, whereas repair semantics provides a more objective framework based on logical consistency.