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Bayesian Description Logics. In:
 Proc. of DL’14. CEUR Workshop Proceedings,
, 2014
"... Abstract This chapter considers, on the one hand, extensions of Description Logics by features not available in the basic framework, but considered important for using Description Logics as a modeling language. In particular, it addresses the extensions concerning: concrete domain constraints; moda ..."
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Cited by 394 (49 self)
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Abstract This chapter considers, on the one hand, extensions of Description Logics by features not available in the basic framework, but considered important for using Description Logics as a modeling language. In particular, it addresses the extensions concerning: concrete domain constraints; modal, epistemic, and temporal operators; probabilities and fuzzy logic; and defaults. On the other hand, it considers nonstandard inference problems for Description Logics, i.e., inference problems thatunlike subsumption or instance checkingare not available in all systems, but have turned out to be useful in applications. In particular, it addresses the nonstandard inference problems: least common subsumer and most specific concept; unification and matching of concepts; and rewriting.
Query Answering for OWLDL with Rules
 Journal of Web Semantics
, 2004
"... Both OWLDL and functionfree Horn rules are decidable fragments of firstorder logic with interesting, yet orthogonal expressive power. A combination of OWLDL and rules is desirable for the Semantic Web; however, it might easily lead to the undecidability of interesting reasoning problems. Here, w ..."
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Cited by 329 (28 self)
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Both OWLDL and functionfree Horn rules are decidable fragments of firstorder logic with interesting, yet orthogonal expressive power. A combination of OWLDL and rules is desirable for the Semantic Web; however, it might easily lead to the undecidability of interesting reasoning problems. Here, we present a decidable such combination where rules are required to be DLsafe: each variable in the rule is required to occur in a nonDLatom in the rule body. We discuss the expressive power of such a combination and present an algorithm for query answering in the related logic SHIQ extended with DLsafe rules, based on a reduction to disjunctive programs.
A general Datalogbased framework for tractable query answering over ontologies
 In Proc. PODS2009. ACM
, 2009
"... Ontologies play a key role in the Semantic Web [4], data modeling, and information integration [16]. Recent trends in ontological reasoning have shifted from decidability issues to tractability ones, as e.g. reflected by the work on the DLLite family of tractable description logics (DLs) [11, 19]. ..."
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Cited by 135 (24 self)
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Ontologies play a key role in the Semantic Web [4], data modeling, and information integration [16]. Recent trends in ontological reasoning have shifted from decidability issues to tractability ones, as e.g. reflected by the work on the DLLite family of tractable description logics (DLs) [11, 19]. An important result of these works is that the main
DL+log: Tight integration of description logics and disjunctive datalog
 In KR2006
, 2006
"... The integration of Description Logics and Datalog rules presents many semantic and computational problems. In particular, reasoning in a system fully integrating Description Logics knowledge bases (DLKBs) and Datalog programs is undecidable. Many proposals have overcomed this problem through a “saf ..."
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Cited by 114 (6 self)
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The integration of Description Logics and Datalog rules presents many semantic and computational problems. In particular, reasoning in a system fully integrating Description Logics knowledge bases (DLKBs) and Datalog programs is undecidable. Many proposals have overcomed this problem through a “safeness ” condition that limits the interaction between the DLKB and the Datalog rules. Such a safe integration of Description Logics and Datalog provides for systems with decidable reasoning, at the price of a strong limitation in terms of expressive power. In this paper we define DL+log, a general framework for the integration of Description Logics and disjunctive Datalog. From the knowledge representation viewpoint, DL+log extends previous proposals, since it allows for a tighter form of integration between DLKBs and Datalog rules which overcomes the main representational limits of the approaches based on the safeness condition. From the reasoning viewpoint, we present algorithms for reasoning in DL+log, and prove decidability and complexity of reasoning in DL+log for several Description Logics. To the best of our knowledge, DL+log constitutes the most powerful decidable combination of Description Logics and disjunctive Datalog rules proposed so far.
A uniform integration of higherorder reasoning and external evaluations in answerset programming
 In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI05
, 2005
"... We introduce HEX programs, which are nonmonotonic logic programs admitting higherorder atoms as well as external atoms, and we extend the wellknown answerset semantics to this class of programs. Higherorder features are widely acknowledged as useful for performing metareasoning, among other task ..."
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Cited by 98 (41 self)
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We introduce HEX programs, which are nonmonotonic logic programs admitting higherorder atoms as well as external atoms, and we extend the wellknown answerset semantics to this class of programs. Higherorder features are widely acknowledged as useful for performing metareasoning, among other tasks. Furthermore, the possibility to exchange knowledge with external sources in a fully declarative framework such as AnswerSet Programming (ASP) is nowadays important, in particular in view of applications in the Semantic Web area. Through external atoms, HEX programs can model some important extensions to ASP, and are a useful KR tool for expressing various applications. Finally, complexity and implementation issues for a preliminary prototype are discussed. 1
A Faithful Integration of Description Logics with Logic Programming
 In Proc. of the 20th Int. Joint Conf. on Artificial Intelligence (IJCAI 2007
"... Integrating description logics (DL) and logic programming (LP) would produce a very powerful and useful formalism. However, DLs and LP are based on quite different principles, so achieving a seamless integration is not trivial. In this paper, we introduce hybrid MKNF knowledge bases that faithfully ..."
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Cited by 91 (8 self)
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Integrating description logics (DL) and logic programming (LP) would produce a very powerful and useful formalism. However, DLs and LP are based on quite different principles, so achieving a seamless integration is not trivial. In this paper, we introduce hybrid MKNF knowledge bases that faithfully integrate DLs with LP using the logic of Minimal Knowledge and Negation as Failure (MKNF) [Lifschitz, 1991]. We also give reasoning algorithms and tight data complexity bounds for several interesting fragments of our logic. 1
Reconciling description logics and rules
, 2010
"... Description logics (DLs) and rules are formalisms that emphasize different aspects of knowledge representation: whereas DLs are focused on specifying and reasoning about conceptual knowledge, rules are focused on nonmonotonic inference. Many applications, however, require features of both DLs and ru ..."
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Cited by 81 (0 self)
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Description logics (DLs) and rules are formalisms that emphasize different aspects of knowledge representation: whereas DLs are focused on specifying and reasoning about conceptual knowledge, rules are focused on nonmonotonic inference. Many applications, however, require features of both DLs and rules. Developing a formalism that integrates DLs and rules would be a natural outcome of a large body of research in knowledge representation and reasoning of the last two decades; however, achieving this goal is very challenging and the approaches proposed thus far have not fully reached it. In this paper, we present a hybrid formalism of MKNF + knowledge bases, which integrates DLs and rules in a coherent semantic framework. Achieving seamless integration is nontrivial, since DLs use an openworld assumption, while the rules are based on a closedworld assumption. We overcome this discrepancy by basing the semantics of our formalism on the logic of minimal knowledge and negation as failure (MKNF) by Lifschitz. We present several algorithms for reasoning with MKNF + knowledge bases, each suitable to different kinds of rules, and establish tight complexity bounds.
Equilibria in Heterogeneous Nonmonotonic MultiContext Systems
 In 22nd AAAI Conference on Artificial Intelligence (AAAI2007
, 2007
"... We propose a general framework for multicontext reasoning which allows us to combine arbitrary monotonic and nonmonotonic logics. Nonmonotonic bridge rules are used to specify the information flow among contexts. We investigate several notions of equilibrium representing acceptable belief states f ..."
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Cited by 79 (17 self)
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We propose a general framework for multicontext reasoning which allows us to combine arbitrary monotonic and nonmonotonic logics. Nonmonotonic bridge rules are used to specify the information flow among contexts. We investigate several notions of equilibrium representing acceptable belief states for our multicontext systems. The approach generalizes the heterogeneous monotonic multicontext systems developed by F. Giunchiglia and colleagues as well as the homogeneous nonmonotonic multicontext systems of Brewka, Serafini and Roelofsen. Background and Motivation Interest in formalizations of contextual information and intercontextual information flow has steadily increased over the last years. Based on seminal papers by McCarthy (1987)
Wellfounded semantics for description logic programs in the Semantic Web
, 2009
"... The realization of the Semantic Web vision, in which computational logic has a prominent role, has stimulated a lot of research on combining rules and ontologies, which are formulated in different formalisms, into a framework that is more useful for describing semantic content. In particular, combin ..."
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Cited by 71 (19 self)
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The realization of the Semantic Web vision, in which computational logic has a prominent role, has stimulated a lot of research on combining rules and ontologies, which are formulated in different formalisms, into a framework that is more useful for describing semantic content. In particular, combining logic programming with the Web Ontology Language (OWL), which is a standard based on description logics, emerged as an important issue for linking the Rules and Ontology Layers of the Semantic Web. Nonmonotonic description logic programs (or dlprograms) were introduced for such a combination, in which a pair (L,P) of a description logic knowledge base L and a set of rules P with negation as failure is given a modelbased semantics that generalizes the answer set semantics of logic programs. In this paper, we reconsider dlprograms and present a wellfounded semantics for them as an analog for the other main semantics of logic programs. It generalizes the canonical definition of the wellfounded semantics based on unfounded sets, and, as we show, lifts many of the wellknown properties from ordinary logic programs to dlprograms. Among these properties: our semantics amounts to a partial model approximating the answer set semantics, which yields for positive and stratified dlprograms a total model coinciding with the answer set semantics; it has polynomial data complexity provided the access to the description logic
Answer Sets
, 2007
"... This chapter is an introduction to Answer Set Prolog a language for knowledge representation and reasoning based on the answer set/stable model semantics of logic programs [44, 45]. The language has roots in declarative programing [52, 65], the syntax and semantics of standard Prolog [24, 23], disj ..."
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Cited by 62 (5 self)
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This chapter is an introduction to Answer Set Prolog a language for knowledge representation and reasoning based on the answer set/stable model semantics of logic programs [44, 45]. The language has roots in declarative programing [52, 65], the syntax and semantics of standard Prolog [24, 23], disjunctive databases [66, 67] and nonmonotonic logic