Hypothetical datalog: complexity and expressibility

Bonner, Anthony J.

doi:10.1016/0304-3975(90)90011-6

Cited by 47 publications

(33 citation statements)

References 10 publications

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“…Especially here, query optimization techniques play a crucial role, and our results suggest to focus further research in this direction. Furthermore, we aim at conducting a proper computational analysis as it has been done for Hypothetical datalog [30], in which truth of atoms is conditioned by hypothetical additions to the dataset at hand. Likewise, our framework allows to add ontological knowledge and rules to datasets before querying: note however that, in the spirit of [31], our framework allows for hypotheses (also called "premises") on a per query basis rather than a per atom basis.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes

Ianni

Krennwallner

Martello

et al. 2009

Lecture Notes in Computer Science

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Abstract. RDF Schema (RDFS) as a lightweight ontology language is gaining popularity and, consequently, tools for scalable RDFS inference and querying are needed. SPARQL has become recently a W3C standard for querying RDF data, but it mostly provides means for querying simple RDF graphs only, whereas querying with respect to RDFS or other entailment regimes is left outside the current specification. In this paper, we show that SPARQL faces certain unwanted ramifications when querying ontologies in conjunction with RDF datasets that comprise multiple named graphs, and we provide an extension for SPARQL that remedies these effects. Moreover, since RDFS inference has a close relationship with logic rules, we generalize our approach to select a custom ruleset for specifying inferences to be taken into account in a SPARQL query. We show that our extensions are technically feasible by providing benchmark results for RDFS querying in our prototype system GiaBATA, which uses Datalog coupled with a persistent Relational Database as a back-end for implementing SPARQL with dynamic rule-based inference. By employing different optimization techniques like magic set rewriting our system remains competitive with state-of-the-art RDFS querying systems.

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Section: Future Work and Conclusionmentioning

confidence: 99%

Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes

Ianni

Krennwallner

Martello

et al. 2009

Lecture Notes in Computer Science

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“…These include semantics of logic programs with negation such as predicate stratification and fixpoint semantics [Apt et al 1988], local stratification and perfect model semantics [Przmusinski 1988], stable model semantics [Gelfond Deductive Database Languages: Problems and Solutions • 29 and Lifschitz 1988;Przymusinski 1990], modularly stratified semantics [Ross 1994], well-founded semantics [Gelder et al 1991] and alternating fixpoint semantics [Gelder 1993]; non-Horn clause logic programs such as disjunctive deductive databases [Fernández and Minker 1992a;Fernández and Minker 1992b;Barback 1992;Subrahmanian 1993], hypothetical reasoning [Bonner 1989;Bonner 1990;Chen 1997;Inoue 1994;Sattar and Goebel 1997]; constraints [Brodsky 1993;Jaffar and Maher 1994;Grumbach and Su 1996], etc. However, we believe that a separate survey is more appropriate for these issues.…”

Section: Introductionmentioning

confidence: 99%

Deductive database languages

Liu

1999

ACM Comput. Surv.

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Deductive databases result from the integration of relational database and logic programming techniques. However, significant problems remain inherent in this simple synthesis from the language point of view. In this paper, we discuss these problems from four different aspects: complex values, object orientation, higherorderness, and updates. In each case, we examine four typical languages that address the corresponding issues.

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“…The implicational extension (with or without negation as failure) of conventional logic programming has attracted the attention of several researchers (see Bonner and McCarty 1990;Bomrer et al 1989;Bonner 1988;Harland 1989;Gabbay and Reyle 1984;Giordano et al 1988;Miller 1989) for many different reasons. Owing to the intuitionistic character of implication in goals, N-Prolog naturally fits in the framework of the so-called intuitionistic logic programming.…”

Section: Introductionmentioning

confidence: 99%

N-Prolog and equivalence of logic programs

Olivetti

Terracini

1992

J Logic Lang Inf

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The aim of this work is to develop adeclarative semantics for N-Prolog with negation as failure. N-Prolog is an extension of Prolog proposed by Reyle (1984, 1985) which allows for occurrences of nested implications in both goals and clauses. Our starting point is an operational semantics of the language defined by means of top-down derivation trees. Negation as finite failure can be naturally introduced in this context. A goal 1G may be inferred from a database if every top-down derivation of G from the database finitely fails, i.e., contains a failure node at finite height.Our purposeis to give alogical interpretation of theunderlying operational semantics. In the present work (Part 1) we take into consideration only the basic problems of determining such an interpretation, so that our analysis will concentrate on the propositional case. Nevertheless we give an intuitive account of how to extend our results to a first order language. A full treatment of N-Prolog with quantifiers will be deferred to the second part of this work.Our main contribution to the logical understanding of N-Prolog is the development of a notion of modal completion for programs, or databases. N-Prolog deductions turn out to be sound and complete with respect to such completions. More exactly, we introduce a natural modal three-valued logic PK and we prove that a goal is derivable from a propositional program if and only if it is implied by the completion of the program in the logic PK. This result holds for arbitrary programs. We assume no syntactic restriction, such as stratification (Apt et al. 1988;Bonner and McCarty 1990). In particular, we allow for arbitrary recursion through negation.Our semantica analysis heavily relies on a notion of intensional equivalence for programs and goals. This notion is naturally induced by the operational semantics, and is preserved under substitution of equivalent subexpressions. Basing on this substitution property we develop a theory of normal forms of programs and goals. Every program can be effectively transformed into an equivalent program in normal form. From the simple and uniform structure of programs in normal form one may directly define the completion.

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Hypothetical datalog: complexity and expressibility

Cited by 47 publications

References 10 publications

Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes

Dynamic Querying of Mass-Storage RDF Data with Rule-Based Entailment Regimes

Deductive database languages

N-Prolog and equivalence of logic programs

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