MKNF-based Hybrid Knowledge Bases (HKBs) integrate Logic Programming (LP) and Description Logics (DLs) ofiering the com-bined expressiveness of the two formalisms. In particular, HKB allow to make different closure assumptions for different predicates. HKBs have been given a well-founded semantics in terms of an alternate fixpoint. In this paper we provide an alternative definition of the semantics using an iterated fixpoint. In this way the computation of the well-founded model proceeds uniformly bottom-up, making the semantics easier to understand, to reason with and to automate. We also present slightly different but equivalent versions of our definition. We then discuss the relationships of HKBs with other formalisms. The results show that over-all HKBs seem to be those that more tightly integrate LP and DL, even if there exist incomparable languages such as the recent FO(ID) formalism.

Iterated fixpoint well-founded semantics for hybrid knowledge bases

ALBERTI, Marco;LAMMA, Evelina;RIGUZZI, Fabrizio;ZESE, Riccardo
2017

Abstract

MKNF-based Hybrid Knowledge Bases (HKBs) integrate Logic Programming (LP) and Description Logics (DLs) ofiering the com-bined expressiveness of the two formalisms. In particular, HKB allow to make different closure assumptions for different predicates. HKBs have been given a well-founded semantics in terms of an alternate fixpoint. In this paper we provide an alternative definition of the semantics using an iterated fixpoint. In this way the computation of the well-founded model proceeds uniformly bottom-up, making the semantics easier to understand, to reason with and to automate. We also present slightly different but equivalent versions of our definition. We then discuss the relationships of HKBs with other formalisms. The results show that over-all HKBs seem to be those that more tightly integrate LP and DL, even if there exist incomparable languages such as the recent FO(ID) formalism.
2017
De-scription Logics; Hybrid Knowledge Bases; MKNF; Well-founded semantics; Computer Science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2377110
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