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469
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 307 (29 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.
Splitting a Logic Program
 Principles of Knowledge Representation
, 1994
"... In many cases, a logic program can be divided into two parts, so that one of them, the \bottom " part, does not refer to the predicates de ned in the \top " part. The \bottom " rules can be used then for the evaluation of the predicates that they de ne, and the computed va ..."
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Cited by 284 (15 self)
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In many cases, a logic program can be divided into two parts, so that one of them, the \bottom &quot; part, does not refer to the predicates de ned in the \top &quot; part. The \bottom &quot; rules can be used then for the evaluation of the predicates that they de ne, and the computed values can be used to simplify the \top &quot; de nitions. We discuss this idea of splitting a program in the context of the answer set semantics. The main theorem shows how computing the answer sets for a program can be simpli ed when the program is split into parts. The programs covered by the theorem may use both negation as failure and classical negation, and their rules may have disjunctive heads. The usefulness of the concept of splitting for the investigation of answer sets is illustrated by several applications. First, we show that a conservative extension theorem by Gelfond and Przymusinska and a theorem on the closed world assumption by Gelfond and Lifschitz are easy consequences of the splitting theorem. Second, (locally) strati ed programs are shown to have a simple characterization in terms of splitting. The existence and uniqueness of an answer set for such a program can be easily derived from this characterization. Third, we relate the idea of splitting to the notion of orderconsistency. 1
ManyValued Modal Logics
 Fundamenta Informaticae
, 1992
"... . Two families of manyvalued modal logics are investigated. Semantically, one family is characterized using Kripke models that allow formulas to take values in a finite manyvalued logic, at each possible world. The second family generalizes this to allow the accessibility relation between worlds a ..."
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Cited by 256 (16 self)
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. Two families of manyvalued modal logics are investigated. Semantically, one family is characterized using Kripke models that allow formulas to take values in a finite manyvalued logic, at each possible world. The second family generalizes this to allow the accessibility relation between worlds also to be manyvalued. Gentzen sequent calculi are given for both versions, and soundness and completeness are established. 1 Introduction The logics that have appeared in artificial intelligence form a rich and varied collection. While classical (and maybe intuitionistic) logic su#ces for the formal development of mathematics, artificial intelligence has found uses for modal, temporal, relevant, and manyvalued logics, among others. Indeed, I take it as a basic principle that an application should find (or create) an appropriate logic, if it needs one, rather than reshape the application to fit some narrow class of `established' logics. In this paper I want to enlarge the variety of logics...
The Complexity of Concept Languages
, 1995
"... The basic feature of Terminological Knowledge Representation Systems is to represent knowledge by means of taxonomies, here called terminologies, and to provide a specialized reasoning engine to do inferences on these structures. The taxonomy is built through a representation language called concept ..."
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Cited by 242 (34 self)
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The basic feature of Terminological Knowledge Representation Systems is to represent knowledge by means of taxonomies, here called terminologies, and to provide a specialized reasoning engine to do inferences on these structures. The taxonomy is built through a representation language called concept language (or description logic), which is given welldefined settheoretic semantics. The efficiency of reasoning has often been advocated as a primary motivation for the use of such systems. Deduction methods and computational properties of reasoning problems in concept languages are the subject of this paper. The main contributions of the paper are: (1) a complexity analysis of concept satisfiability and subsumption for a wide class of concept languages; (2) the algorithms for these inferences that comply with the worstcase complexity of the reasoning task they perform.
Decidable reasoning in terminological knowledge representation systems
 Journal of Artificial Intelligence Research
, 1993
"... Terminological Knowledge Representation Systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). The TKRS we consider in this paper is of practical interest since it goes beyond the capabilities of presently available TKRSs. ..."
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Cited by 203 (12 self)
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Terminological Knowledge Representation Systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). The TKRS we consider in this paper is of practical interest since it goes beyond the capabilities of presently available TKRSs. First, our TKRS is equipped with a highly expressive concept, language, called ALCNR, including general complements of concepts, number restrictions and role conjunction. Second, it allows one to express inclusion statements between general concepts, in particular to express terminological cycles. We provide a sound, complete and terminating calculus for reasoning in ALCNRknowledge bases based on the general technique of constraint systems.
ALlog: Integrating Datalog and Description Logics
 JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
, 1998
"... We presenan integrated system for knowledge representation, called ALlog, based on description logics and the deductive database language Datalog. ALlog embodies two subsystems, called structural and relational. The former allows for the definition of structural knowledge about classes of interest ..."
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Cited by 166 (12 self)
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We presenan integrated system for knowledge representation, called ALlog, based on description logics and the deductive database language Datalog. ALlog embodies two subsystems, called structural and relational. The former allows for the definition of structural knowledge about classes of interest #concepts# and membership relation between objects and classes. The latter allows for the definition of relational knowledge about objects described in the structural component. The interaction between the two components is obtained by allowing constraints within Datalog clauses, thus requiring the variables in the clauses to range over the set of instances of a specified concept. We propose a method for query answering in ALlog based on constrained resolution, where the usual deduction procedure defined for Datalog is integrated with a method for reasoning on the structural knowledge.
Static Analysis of Executables to Detect Malicious Patterns
 In Proceedings of the 12th USENIX Security Symposium
, 2003
"... Malicious code detection is a crucial component of any defense mechanism. In this paper, we present a unique viewpoint on malicious code detection. We regard malicious code detection as an obfuscationdeobfuscation game between malicious code writers and researchers working on malicious code detecti ..."
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Cited by 135 (0 self)
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Malicious code detection is a crucial component of any defense mechanism. In this paper, we present a unique viewpoint on malicious code detection. We regard malicious code detection as an obfuscationdeobfuscation game between malicious code writers and researchers working on malicious code detection. Malicious code writers attempt to obfuscate the malicious code to subvert the malicious code detectors, such as antivirus software. We tested the resilience of three commercial virus scanners against codeobfuscation attacks. The results were surprising: the three commercial virus scanners could be subverted by very simple obfuscation transformations! We present an architecture for detecting malicious patterns in executables that is resilient to common obfuscation transformations. Experimental results demonstrate the efficacy of our prototype tool, SAFE (a static analyzer for executables). 1
A Survey on Knowledge Compilation
, 1998
"... this paper we survey recent results in knowledge compilation of propositional knowledge bases. We first define and limit the scope of such a technique, then we survey exact and approximate knowledge compilation methods. We include a discussion of compilation for nonmonotonic knowledge bases. Keywor ..."
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Cited by 115 (4 self)
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this paper we survey recent results in knowledge compilation of propositional knowledge bases. We first define and limit the scope of such a technique, then we survey exact and approximate knowledge compilation methods. We include a discussion of compilation for nonmonotonic knowledge bases. Keywords: Knowledge Representation, Efficiency of Reasoning
Representation and Inference for Natural language  A First Course in . . .
, 1999
"... 3.672> X with the complex term 1 + 1, not with 2, which, for people unused to Prolog's little ways, tends to come as a bit of a surprise. If we want to carry out the actual arithmetic involved, we have to explicitly force evaluation by making use of the very special inbuilt `operator' ..."
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Cited by 96 (11 self)
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3.672> X with the complex term 1 + 1, not with 2, which, for people unused to Prolog's little ways, tends to come as a bit of a surprise. If we want to carry out the actual arithmetic involved, we have to explicitly force evaluation by making use of the very special inbuilt `operator' is/2. This calls an inbuilt mechanism which carries out the arithmetic evaluation of its second argument, and then unication plays no role here!). On the other hand, \== checks whether its argument are not identical. Arithmetic Prolog contains some builtin operators for handling integer arithmetic. These include *, / +,  (for multiplication, division, addition, and subtraction, respectively) and >, < for comparing numbers. These symbols, however, are just ordinary Prolog operators. That is, they are just a user friendly notation for writing