Abstract. We define a boolean complete description logic dialect called DLFDreg that can be used to reason about structural equality in semistructured ordered data in the presence of document type definitions. This application depends on the novel ability of DLFDreg to express functional dependencies over sets of possibly infinite feature paths defined by regular languages. We also present a decision procedure for the associated logical implication problem. The procedure underlies a mapping of such problems to satisfiability problems of Datalog ∨,¬ nS and in turn to the Ackermann case of the decision problem. 1

In this paper, we present a novel formalism of hybrid MKNF knowledge bases, which allows us to seamlessly integrate an arbitrary decidable description logic with logic programming rules. We thus obtain a powerful hybrid formalism that combines the best features of both description logics, such as the ability to model taxonomic knowledge, and logic programming, such as the ability to perform nonmonotonic reasoning. Extending DLs with unrestricted rules makes reasoning undecidable. To obtain decidability, we apply the well-known DL-safety restriction that makes the rules applicable only to explicitly named individuals, and thus trade some expressivity for decidability. We present several reasoning algorithms for different fragments of our logic, as well as the corresponding complexity results. Our results show that, in many cases, the data complexity of reasoning with hybrid MKNF knowledge bases is not higher than the data complexity of reasoning

With the advent of the World Wide Web, a vast number of heterogenous information sources has become available. In order to access and process these data, suitable tools and methods for building an information infrastructure are necessary. One task for this purpose is the selection of relevant information sources in automated query answering.

In this work we present a backjumping technique for Disjunctive Logic Programming under the Stable Model Semantics (SDLP). It builds upon related techniques that had originally been introduced for constraint solving, which have been adapted to propositional satisfiability testing, and recently also to non-disjunctive logic programming under the stable model semantics (SLP) [1, 2]. We focus on backjumping without clause learning, providing a new theoretical framework for backjumping in SDLP, elaborating on and exploiting peculiarities of the disjunctive setting. We present a reason calculus and associated computations, which – compared to the traditional approaches – reduces the information to be stored, while fully preserving the correctness and the efficiency of the backjumping technique, handling specific aspects of disjunction in a benign way. We implemented the proposed technique in DLV, the state-of-the-art SDLP system. We have conducted several experiments on hard random and structured instances in order to assess the impact of backjumping, using DLV with and without the backjumping method described in this paper, using as a parameter to both two different heuristic functions. Our

We describe the elds of disjunctive logic programming and disjunctive deductive databases from the time of their inception to the current time. Contributions with respect to semantics, implementations and applications are surveyed.

The paper presents DLV +, a Disjunctive Logic Programming (DLP) system with object-oriented constructs, including classes, objects, (multiple) inheritance, and types. DLV + is built on top of DLV (a state-of-the art DLP system), and provides a graphical user interface that allows one to specify, update, browse, query, and reason on knowledge bases. Two strong points of the system are the powerful type-checking mechanism and the advanced interface for visual querying. DLV + is already used for the development of knowledge based applications for information extraction and text classification.

We present a decision procedure for the logical implication problem of a boolean complete DL dialect that includes attributes, roles, inverse roles and a new concept constructor that is capable of expressing a variety of equality and order-generating dependencies. The procedure underlies a mapping of such problems to satisfiability problems of Datalog # nS and in turn to the Ackermann case of the decision problem. 1

Deductive database systems have made major advances on efficient support for nonmonotonic reasoning. A first generation of deductive database systems supported the notion of stratification for programs with negation and set aggregates. Stratification is simple to understand and efficient to implement but it is too restrictive; therefore, a second generation of systems seeks efficient support for more powerful semantics based on notions such as well-founded models and stable models. In this respect, a particularly powerful set of constructs is provided by the recently enhanced LDL++ system that supports (i) monotonic user-defined aggregates, (ii) XY-stratified programs, and (iii) the nondeterministic choice constructs under stable model semantics. This integrated set of primitives supports a terse formulation and efficient implementation for complex computations, such as greedy algorithms and data mining functions, yielding levels of expressive power unmatched by other deductive...

Disjunction is generally considered to add expressive power to logic programs under the stable model semantics, which have become a popular programming paradigm for knowledge representation and reasoning. However, disjunction is often not really needed, in that an equivalent program without disjunction can be given. In this paper, we consider the question, given a disjunctive logic program, P , under which conditions does there exist an equivalent normal (i.e., disjunction-free) logic program P . In fact, we study this problem under different notions of equivalence, viz. for ordinary equivalence (considering the collections of all stable models of the programs) as well as for the more restrictive notions of strong and uniform equivalence. We resolve the issue for propositional programs on which we focus here, and present a simple, appealing semantic criterion from which all disjunctions can be eliminated under strong equivalence; testing this criterion is coNP-complete, but the class of programs satisfying it has the same complexity as disjunctive logic programs in general. We also show that under ordinary and uniform equivalence, disjunctions can always be eliminated. In all cases, we give constructive methods to achieve this. However, we also provide evidence that disjunctive logic programs are a more succinct knowledge representation formalism than normal logic programs under all these notions of equivalence.

We present an agent monitoring approach, which aims at refuting from (possibly incomplete) information at hand that a multi-agent system (MAS) is implemented properly. In this approach, agent collaboration is abstractly described in an action theory. Action sequences reaching the collaboration goal are determined by a planner, whose compliance with the actual MAS behavior allows to detect possible collaboration failures. The approach can be fruitfully applied to aid offline testing of a MAS implementation, as well as online monitoring.