Results 1  10
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226
The Utility of Knowledge in Inductive Learning
, 1992
"... In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating constantfree Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these types of knowledge have on the cost of learning a r ..."
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Cited by 154 (22 self)
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In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating constantfree Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these types of knowledge have on the cost of learning a
Uniform proofs as a foundation for logic programming
 ANNALS OF PURE AND APPLIED LOGIC
, 1991
"... A prooftheoretic characterization of logical languages that form suitable bases for Prologlike programming languages is provided. This characterization is based on the principle that the declarative meaning of a logic program, provided by provability in a logical system, should coincide with its ..."
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Cited by 428 (122 self)
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. Horn clauses are then generalized to hereditary Harrop formulas and it is shown that firstorder and higherorder versions of this new class of formulas are also abstract logic programming languages if the inference rules are those of either intuitionistic or minimal logic. The programming language
Probabilistic Horn abduction and Bayesian networks
 Artificial Intelligence
, 1993
"... This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesia ..."
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Cited by 328 (38 self)
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This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete
Canonical Sets of Horn Clauses
, 1990
"... Rewrite rules are oriented equations used to replace equalsbyequals in the specified direction. Input terms are repeatedly rewritten according to the rules. When and if no rule applies... ..."
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Cited by 6 (2 self)
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Rewrite rules are oriented equations used to replace equalsbyequals in the specified direction. Input terms are repeatedly rewritten according to the rules. When and if no rule applies...
UncertaintyValued Horn Clauses
, 1994
"... There are many forms of uncertainty, each usually again having more than one theoretical model. Therefore, a very flexible kind of uncertaintyvalued Horn clauses is introduced in RELFUN in section 1. They have a head, several premises and an uncertainty factor, which represents the uncertainty of t ..."
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Cited by 1 (0 self)
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There are many forms of uncertainty, each usually again having more than one theoretical model. Therefore, a very flexible kind of uncertaintyvalued Horn clauses is introduced in RELFUN in section 1. They have a head, several premises and an uncertainty factor, which represents the uncertainty
Horn Clause Contraction Functions
"... In classical, AGMstyle belief change, it is assumed that the underlying logic contains classical propositional logic. This is clearly a limiting assumption, particularly in Artificial Intelligence. Consequently there has been recent interest in studying belief change in approaches where the full e ..."
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Cited by 2 (0 self)
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and postulate set, linking them via a representation result. Last, we investigate the closelyrelated notion of forgetting in Horn clauses. This work is arguably interesting since Horn clauses have found widespread use in AI; as well, the results given here may potentially be extended to other areas which make
A Proposal for an OWL Rules Language
 In Proc. of the Thirteenth International World Wide Web Conference (WWW 2004
, 2004
"... Although the OWLWeb Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. We present ORL (OWL Rules Language), a Horn clause rules extension to OWL that overcomes many of these lim ..."
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Cited by 172 (12 self)
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Although the OWLWeb Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. We present ORL (OWL Rules Language), a Horn clause rules extension to OWL that overcomes many
Learning FirstOrder Horn Clauses from Web Text
"... Even the entire Web corpus does not explicitly answer all questions, yet inference can uncover many implicit answers. But where do inference rules come from? This paper investigates the problem of learning inference rules from Web text in an unsupervised, domainindependent manner. The SHERLOCK syst ..."
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Cited by 65 (7 self)
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system, described herein, is a firstorder learner that acquires over 30,000 Horn clauses from Web text. SHERLOCK embodies several innovations, including a novel rule scoring function based on Statistical Relevance (Salmon et al., 1971) which is effective on ambiguous, noisy and incomplete Web
Ordered Completion with Selection for Horn Clauses
, 1995
"... this article Bachmair and Ganzinger proved refutational completeness of an inference system (which also includes one of the inference rules iEquality Factoringj or iMerging Paramodulationj) for ørstorder clauses with equality. This means that refutational completeness of OC S is a consequence of re ..."
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this article Bachmair and Ganzinger proved refutational completeness of an inference system (which also includes one of the inference rules iEquality Factoringj or iMerging Paramodulationj) for ørstorder clauses with equality. This means that refutational completeness of OC S is a consequence
.4 Turing Completeness Of Horn Clauses
"... er a program P, a goal A, and a computation rule R. Soundness of SLD resolution: If a substitution q is the c.a.s. of an SLD refutation for P, A, and R then P = "(Aq). Completeness of the SLD resolution: If a substitution q for the variables of A is such that P = "(Aq) then there e ..."
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er a program P, a goal A, and a computation rule R. Soundness of SLD resolution: If a substitution q is the c.a.s. of an SLD refutation for P, A, and R then P = "(Aq). Completeness of the SLD resolution: If a substitution q for the variables of A is such that P = "
Results 1  10
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