Hybrid MKNF Knowledge Base

Hybrid MKNF (minimal knowledge and negation as failure) knowledge bases integrate rule-based reasoning (like Answer Set Programming) with ontologies, enabling sophisticated modeling of real-world systems requiring both categorical and normative knowledge. Current research focuses on developing efficient reasoning algorithms, particularly conflict-driven solvers and fixpoint characterizations, to handle the computational complexity of these hybrid systems, including extensions to disjunctive rules and three-valued semantics to represent uncertainty. This work is significant for improving the expressiveness and scalability of knowledge representation and reasoning systems, with potential applications in diverse fields requiring complex knowledge integration and reasoning.