Forgetting for distance-based reasoning and repair in DL-Lite

In this paper, we present a forgetting-based approach to handling inconsistency in DL-Lite. Our proposed approach cannot only characterize distance-based reasoning, which is proven to rationally draw meaningful conclusions even from inconsistent DL-Lite knowledge bases but also recovery the consistency of DL-Lite knowledge bases. We first present vectors forgetting for DL-Lite by extending predicates forgetting in DL-Lite and show the predicates to be forgotten which are obtained by computing minimal hitting sets. Moreover, we develop algorithms to compute those predicates by employing Reiter's HS-tree method and then analyze the computational complexity of those proposed algorithms. Finally, we implement our proposed algorithms and evaluate them on both consistent and inconsistent ontologies. Besides, we discuss some applications of vectors forgetting in privacy protection.