Lean kernels (LKs) are an effective optimization for deriving the causes of unsatisfiability of a propositional formula. Interestingly, no analogous notion exists for explaining consequences of description logic (DL) ontologies. We introduce LKs for DLs using a general notion of consequence-based methods, and provide an algorithm for computing them which incurs in only a linear time overhead. As an example, we instantiate our framework to the DL ALC. We prove formally and empirically that LKs provide a tighter approximation of the set of relevant axioms for a consequence than syntactic locality-based modules.
CITATION STYLE
Peñaloza, R., Mencía, C., Ignatiev, A., & Marques-Silva, J. (2017). Lean kernels in description logics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10249 LNCS, pp. 518–533). Springer Verlag. https://doi.org/10.1007/978-3-319-58068-5_32
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