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- 2007 (15) (entfernen)
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Probability propagation nets
(2007)
A class of high level Petri nets, called "probability propagation nets", is introduced which is particularly useful for modeling probability and evidence propagation. These nets themselves are well suited to represent the probabilistic Horn abduction, whereas specific foldings of them will be used for representing the flows of probabilities and likelihoods in Bayesian networks.
UML models and OWL ontologies constitute modeling approaches with different strength and weaknesses that make them appropriate for use of specifying different aspects of software systems. In particular, OWL ontologies are well suited to specify classes using an expressive logical language with highly flexible, dynamic and polymorphic class membership, while UML diagrams are much more suitable for specifying not only static models including classes and associations, but also dynamic behavior. Though MOF based metamodels and UML profiles for OWL have been proposed in the past, an integrated use of both modeling approaches in a coherent framework has been lacking so far. We present such a framework, TwoUse, for developing integrated models, comprising the benefits of UML models and OWL ontologies
We introduce linear expressions for unrestricted dags (directed acyclic graphs) and finite deterministic and nondeterministic automata operating on them. Those dag automata are a conservative extension of the Tu,u-automata of Courcelle on unranked, unordered trees and forests. Several examples of dag languages acceptable and not acceptable by dag automata and some closure properties are given.
Networked RDF graphs
(2007)
Networked graphs are defined in this paper as a small syntactic extension of named graphs in RDF. They allow for the definition of a graph by explicitly listing triples as well as by SPARQL queries on one or multiple other graphs. By this extension it becomes possible to define a graph including a view onto other graphs and to define the meaning of a set of graphs by the way they reference each other. The semantics of networked graphs is defined by their mapping into logic programs. The expressiveness and computational complexity of networked graphs, varying by the set of constraints imposed on the underlying SPARQL queries, is investigated. We demonstrate the capabilities of networked graphs by a simple use case.
Generalized methods for automated theorem proving can be used to compute formula transformations such as projection elimination and knowledge compilation. We present a framework based on clausal tableaux suited for such tasks. These tableaux are characterized independently of particular construction methods, but important features of empirically successful methods are taken into account, especially dependency directed backjumping and branch local operation. As an instance of that framework an adaption of DPLL is described. We show that knowledge compilation methods can be essentially improved by weaving projection elimination partially into the compilation phase.