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Can you tell the difference between DLLite ontologies
 IN PROCEEDINGS OF KR’08
, 2008
"... We develop a formal framework for comparing different versions of DLLite ontologies. Four notions of difference and entailment between ontologies are introduced and their applications in ontology development and maintenance discussed. These notions are obtained by distinguishing between differences ..."
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Cited by 49 (5 self)
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We develop a formal framework for comparing different versions of DLLite ontologies. Four notions of difference and entailment between ontologies are introduced and their applications in ontology development and maintenance discussed. These notions are obtained by distinguishing between differences that can be observed among concept inclusions, answers to queries over ABoxes, and by taking into account additional context ontologies. We compare these notions, study their metaproperties, and determine the computational complexity of the corresponding reasoning tasks. Moreover, we show that checking difference and entailment can be automated by means of encoding into QBF satisfiability and using offtheshelf QBF solvers. Finally, we explore the relationship between the notion of forgetting (or uniform interpolation) and our notions of difference between ontologies.
Forgetting and Uniform Interpolation in LargeScale Description Logic Terminologies
"... We develop a framework for forgetting concepts and roles (aka uniform interpolation) in terminologies in the lightweight description logic EL extended with role inclusions and domain and range restrictions. Three different notions of forgetting, preserving, respectively, concept inclusions, concept ..."
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Cited by 43 (7 self)
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We develop a framework for forgetting concepts and roles (aka uniform interpolation) in terminologies in the lightweight description logic EL extended with role inclusions and domain and range restrictions. Three different notions of forgetting, preserving, respectively, concept inclusions, concept instances, and answers to conjunctive queries, with corresponding languages for uniform interpolants are investigated. Experiments based on SNOMED CT (Systematised Nomenclature of
Semantic Forgetting in Answer Set Programming
, 2008
"... The notion of forgetting, also known as variable elimination, has been investigated extensively in the context of classical logic, but less so in (nonmonotonic) logic programming and nonmonotonic reasoning. The few approaches that exist are based on syntactic modifications of a program at hand. In t ..."
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Cited by 29 (9 self)
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The notion of forgetting, also known as variable elimination, has been investigated extensively in the context of classical logic, but less so in (nonmonotonic) logic programming and nonmonotonic reasoning. The few approaches that exist are based on syntactic modifications of a program at hand. In this paper, we establish a declarative theory of forgetting for disjunctive logic programs under answer set semantics that is fully based on semantic grounds. The suitability of this theory is justified by a number of desirable properties. In particular, one of our results shows that our notion of forgetting can be entirely captured by classical forgetting. We present several algorithms for computing a representation of the result of forgetting, and provide a characterization of the computational complexity of reasoning from a logic program under forgetting. As applications of our approach, we present a fairly general framework for resolving conflicts in inconsistent knowledge bases that are represented by disjunctive logic programs, and we show how the semantics of inheritance logic programs and update logic programs from the literature can be characterized through forgetting. The basic idea of the conflict resolution framework is to weaken the preferences of each agent by forgetting certain knowledge that causes inconsistency. In particular, we show how to use the notion of forgetting to provide an elegant solution for preference elicitation in disjunctive logic programming.
Logicbased ontology comparison and module extraction, with an application to DLLite
 ARTIFICIAL INTELLIGENCE
, 2010
"... We develop a formal framework for comparing different versions of DLLite ontologies. The main feature of our approach is that we take into account the vocabulary ( = signature) with respect to which one wants to compare ontologies. Five variants of difference and inseparability relations between on ..."
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Cited by 28 (11 self)
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We develop a formal framework for comparing different versions of DLLite ontologies. The main feature of our approach is that we take into account the vocabulary ( = signature) with respect to which one wants to compare ontologies. Five variants of difference and inseparability relations between ontologies are introduced and their respective applications for ontology development and maintenance discussed. These variants are obtained by generalising the notion of conservative extension from mathematical logic and by distinguishing between differences that can be observed among concept inclusions, answers to queries over ABoxes, by taking into account additional context ontologies, and by considering a modeltheoretic, languageindependent notion of difference. We compare these variants, study their metaproperties, determine the computational complexity of the corresponding reasoning tasks, and present decision algorithms. Moreover, we show that checking inseparability can be automated by means of encoding into QBF satisfiability and using offtheshelf general purpose QBF solvers. Inseparability relations between ontologies are then used to develop a formal framework for (minimal) module extraction. We demonstrate that different types of minimal modules induced by these inseparability relations can be automatically extracted from realworld mediumsize DLLite ontologies by composing the tractable syntactic localitybased module extraction algorithm with nontractable extraction algorithms using the multiengine QBF solver aqme. Finally, we explore the relationship between uniform interpolation (or forgetting) and inseparability between ontologies.
Boolean games revisited
 In Proc. ECAI ’06
, 2006
"... Abstract. Game theory is a widely used formal model for studying strategical interactions between agents. Boolean games [7] are two players, zerosum static games where player’s utility functions are binary and described by a single propositional formula, and the strategies available to a player co ..."
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Cited by 23 (4 self)
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Abstract. Game theory is a widely used formal model for studying strategical interactions between agents. Boolean games [7] are two players, zerosum static games where player’s utility functions are binary and described by a single propositional formula, and the strategies available to a player consist of truth assignments to each of a given set of propositional variables (the variables controlled by the player.) We generalize the framework to nplayers games which are not necessarily zerosum. We give simple characterizations of Nash equilibria and dominated strategies, and investigate the computational complexity of the related problems. 1
Forgetting Concepts in DLLite
"... Abstract. To support the reuse and combination of ontologies in Semantic Web applications, it is often necessary to obtain smaller ontologies from existing larger ontologies. In particular, applications may require the omission of many terms, e.g., concept names and role names, from an ontology. How ..."
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Cited by 21 (6 self)
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Abstract. To support the reuse and combination of ontologies in Semantic Web applications, it is often necessary to obtain smaller ontologies from existing larger ontologies. In particular, applications may require the omission of many terms, e.g., concept names and role names, from an ontology. However, the task of omitting terms from an ontology is challenging because the omission of some terms may affect the relationships between the remaining terms in complex ways. We present the first solution to this problem by adapting the technique of forgetting, previously used in other domains. Specifically, we present a semantic definition of forgetting for description logics in general, which generalizes the standard definition for classical logic. We then introduce algorithms that implement forgetting in both DLLite TBoxes and ABoxes, and in DLLite knowledge bases. We prove that the algorithms are correct with respect to the semantic definition of forgetting, and that they run in polynomial time. 1
Dependencies between players in Boolean games
 In Proc. ECSQARU ’07, volume 4724 of LNCS
, 2007
"... Boolean games are a logical setting for representing static games in a succinct way, taking advantage of the expressive power and succinctness of propositional logic. A Boolean game consists of a set of players, each of them controlling a set of propositional variables and having a specific goal ex ..."
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Cited by 21 (1 self)
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Boolean games are a logical setting for representing static games in a succinct way, taking advantage of the expressive power and succinctness of propositional logic. A Boolean game consists of a set of players, each of them controlling a set of propositional variables and having a specific goal expressed by a propositional formula, or more generally a specification of the player’s preference relation in some logical language for compact preference representation, such as prioritized goals. There is a lot of graphical structure hidden in a Boolean game: the satisfaction of each player’s goal depends on players whose actions have an influence on her goals. Exploiting this dependency structure facilitates the computation of pure Nash equilibria, by partly decomposing a game into several subgames that are only loosely related. Key words: Game theory, compact preference representation, problem decomposition 1
Redundancy in logic I: CNF propositional formulae.
 Artif. Intell.,
, 2005
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Foundations for uniform interpolation and forgetting in expressive description logics. In:
 Proceedings of the International Joint Conference on Articial Intelligence (IJCAI11).
, 2011
"... Abstract We study uniform interpolation and forgetting in the description logic ALC. Our main results are modeltheoretic characterizations of uniform interpolants and their existence in terms of bisimulations, tight complexity bounds for deciding the existence of uniform interpolants, an approach ..."
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Cited by 18 (2 self)
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Abstract We study uniform interpolation and forgetting in the description logic ALC. Our main results are modeltheoretic characterizations of uniform interpolants and their existence in terms of bisimulations, tight complexity bounds for deciding the existence of uniform interpolants, an approach to computing interpolants when they exist, and tight bounds on their size. We use a mix of modeltheoretic and automatatheoretic methods that, as a byproduct, also provides characterizations of and decision procedures for conservative extensions.
Compact preference representation for boolean games
 In Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI
, 2006
"... Abstract. Boolean games, introduced by [15, 14], allow for expressing compactly twoplayers zerosum static games with binary preferences: an agent’s strategy consists of a truth assignment of the propositional variables she controls, and a player’s preferences is expressed by a plain propositional ..."
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Cited by 16 (4 self)
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Abstract. Boolean games, introduced by [15, 14], allow for expressing compactly twoplayers zerosum static games with binary preferences: an agent’s strategy consists of a truth assignment of the propositional variables she controls, and a player’s preferences is expressed by a plain propositional formula. These restrictions (twoplayers, zerosum, binary preferences) strongly limit the expressivity of the framework. While the first two can be easily encompassed by defining the agents ’ preferences as an arbitrary nuple of propositional formulas, relaxing the last one needs Boolean games to be coupled with a propositional language for compact preference representation. In this paper, we consider generalized Boolean games where players ’ preferences are expressed within two of these languages: prioritized goals and propositionalized CPnets. 1