A Goal-Directed Implementation of Query Answering for Hybrid MKNF Knowledge Bases

Ontologies and rules are usually loosely coupled in knowledge representation formalisms. In fact, ontologies use open-world reasoning while the leading semantics for rules use non-monotonic, closed-world reasoning. One exception is the tightly-coupled framework of Minimal Knowledge and Negation as Failure (MKNF), which allows statements about individuals to be jointly derived via entailment from an ontology and inferences from rules. Nonetheless, the practical usefulness of MKNF has not always been clear, although recent work has formalized a general resolution-based method for querying MKNF when rules are taken to have the well-founded semantics, and the ontology is modeled by a general oracle. That work leaves open what algorithms should be used to relate the entailments of the ontology and the inferences of rules. In this paper we provide such algorithms, and describe the implementation of a query-driven system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic) rules under the well-founded semantics and a (monotonic) ontology, represented by a CDF Type-1 (ALQ) theory. To appear in Theory and Practice of Logic Programming (TPLP

Les intellectuels hongrois et la Révolution française (1810-1849)

Lukacsy Sandor. Les intellectuels hongrois et la Révolution française (1810-1849). In: Annales historiques de la Révolution française, n°212, 1973. La Hongrie des Lumières à 1848. pp. 264-284

Generating random samples from user-defined distributions

Generating random samples in Stata is very straightforward if the distribution drawn from is uniform or normal. With any other distribution, an inverse method can be used; but even in this case, the user is limited to the built- in functions. For any other distribution functions, their inverse must be derived analytically or numerical methods must be used if analytical derivation of the inverse function is tedious or impossible. In this article, I introduce a command that generates a random sample from any user-specified distribution function using numeric methods that make this command very generic.rsample, random sample, user-defined distribution function, inverse method, Monte Carlo exercise

The Semantic Web Explained : The Technology and Mathematics behind Web 3.0

The Semantic Web is a new area of research and development in the field of computer science, which aims to make it easier for computers to process the huge amount of information on the Web, and indeed other large databases, by enabling computers not only to read, but also understand the information. This book is intended to be a textbook about the Semantic Web and related topics, and is based on successful courses taught by the authors. They describe not only the theoretical issues underlying the semantic web, but also practical matters (such as algorithms, optimisation ideas and implementation details) and this aspect will make the book valuable as well to practitioners. Supplementary materials available via the web include include source the code of program examples, and the syntactic description of various language

The semantic web explained: The technology and mathematics behind Web 3.0

Cambridge, UKvii, 471 p.: bibl. ref., index; 25 c