Enhancing Context Knowledge Repositories with Justifiable Exceptions (Extended Abstract)

Enhancing Context Knowledge Repositories with Justifiable Exceptions (Extended Abstract)

Loris Bozzato, Thomas Eiter, Luciano Serafini

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Journal track. Pages 5568-5572. https://doi.org/10.24963/ijcai.2018/786

The Contextualized Knowledge Repository (CKR) framework was conceived as a logic-based approach for representing context dependent knowledge, which is a well-known area of study in AI. The framework has a two-layer structure with a global context that contains context-independent knowledge and meta-information about the contexts, and a set of local contexts with specific knowledge bases. In many practical cases, it is desirable that inherited global knowledge can be "overridden" at the local level. In order to address this need, we present an extension of CKR with global defeasible axioms: these axioms locally apply to (tuples of) individuals unless an exception for overriding exists; such an exception, however, requires a justification that is provable from the knowledge base. We formalize this intuition and study its semantic and computational properties. Furthermore, we present a translation of extended CKRs to datalog programs under the answer set (i.e., stable) semantics and we present an implementation prototype. Our work adds to the body of results on using deductive database technology in these areas, and provides an expressive formalism for exception handling by overriding.
Keywords:
Knowledge Representation and Reasoning: Non-monotonic Reasoning
Knowledge Representation and Reasoning: Description Logics and Ontologies
Knowledge Representation and Reasoning: Logics for Knowledge Representation