We show how to interoperate, semantically and inferentially, between the leading Semantic Web approaches to rules (RuleML Logic Programs) and ontologies (OWL/DAML+OIL Description Logic) via analyzing their expressive intersection. To do so, we define a new intermediate knowledge representation (KR) contained within this intersection: Description Logic Programs (DLP), and the closely related Description Horn Logic (DHL) which is an expressive fragment of first-order logic (FOL). DLP provides a significant degree of expressiveness, substantially greater than the RDFSchema fragment of Description Logic.

Advanced applications require construction, efficient access and management of large databases with rich data structures and inference mechanisms. However, such capabilities are not directly supported by the existing database systems. In this paper, we describe Relationlog, a persistent deductive database system that is able to directly support the storage, efficient access and inference of data with complex structures.

In this chapter, we summarize the research on querying inconsistent databases we have been conducting over the last five years. The formal framework we have used is based on two concepts: repair and consistent query answer. We describe different approaches to the issue of computing consistent query answers: query transformation, logic programming, inference in annotated logics, and specialized algorithms. We also characterize the computational complexity of this problem. Finally, we discuss related research in artificial intelligence, databases, and logic programming.

SLD resolution with negation as finite failure (SLDNF) reflects the procedural interpretation of predicate calculus as a programming language and forms the computational basis for Prolog systems. Despite its advantages for stack-based memory management, SLDNF is often not appropriate for query evaluation for three reasons: a) it may not terminate due to infinite positive recursion; b) it may not terminate due to infinite recursion through negation; c) it may repeatedly evaluate the same literal in a rule body, leading to unacceptable performance. We address three problems fir a goal-oriented query evaluation of general logic programs by presenting tabled evaluation with delaying (SLG resolution).

... this paper we provide the formal underpinnings for that goal, by formalizing the concepts of negotiation protocols, strategies, and interoperation. We show how to model the information flow of a negotiation, for use in analyzing strategy interoperation. We also present two large sets of strategies whose members all interoperate with one another, and show that these sets contain many practical strategies. We develop the theory both for black-box propositional credentials and credentials with internal structure, and for access control policies whose contents are (resp. are not) sensitive. We also discuss how these results fit into TrustBuilder, our prototype system for trust negotiation

The World Wide Web’s phenomenal growth rate is making it increasingly difficult to locate, organize, and integrate the available information. To cope with this enormous quantity of data, we need to hand off portions of these tasks to machines. However, since natural language processing is still an unsolved problem, machines cannot understand the web pages to the extent required to perform the desired tasks. An alternative is to change the Web to make it more understandable by machines, thereby creating a Semantic Web. Many researchers believe that the key to building this new web lies in the development of semantically-enriched languages. Early languages, such as RDF, SHOE, and Ontobroker, have led to more recent efforts, such as the DARPA Agent Markup Language (DAML). It is argued that languages such as these will revolutionize the Web, but if so, how will the new Web work? In this article, we will put a Semantic Web language through its paces and try to answer questions about how it can be used in practice. How are the semantic descriptions generated? How are these descriptions discovered by agents? How can information from different sites be integrated? How can the Semantic Web be queried? We will present a system that addresses these questions, and describe a suite of tools to help users in their various interactions with the

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, thatrunsfora class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have beendeveloped independently in each research eld. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can signi cantly outperform the Inside-Outside algorithm. 1.

The well-founded model provides a natural and robust semantics for logic programs with negative literals in rule bodies. Although various procedural semantics have been proposed for query evaluation under the well-founded semantics, the practical issues of implementation for effective and efficient computation of queries have been rarely discussed. This paper investigates two major implementation issues of query evaluation under the well-founded semantics, namely (a) to ensure that negative literals be resolved only after their positive counterparts have been completely evaluated, and (b) to detect and handle potential negative loops. We present efficient incremental algorithms for maintaining positive and negative dependencies among subgoals in a top-down evaluation. Both completely evaluated subgoals and potential negative loops are detected by inspecting the dependency information of a single subgoal. Our implementation can be viewed as an effective successor to SLDNF resolution, ex...

We present ProB, an animation and model checking tool for the B method. ProB's animation facilities allow users to gain confidence in their specifications, and unlike the animator provided by the B-Toolkit, the user does not have to guess the right values for the operation arguments or choice variables. ProB contains a model checker and a constraint-based checker, both of which can be used to detect various errors in B specifications. We present our first experiences in using ProB on several case studies, highlighting that ProB enables users to uncover errors that are not easily discovered by existing tools.

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