Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning ML...
Inspired by “GoogleTM Sets” and Bayesian sets, we consider the problem of retrieving complex objects and relations among them, i.e., ground atoms from a logical concept, given...
Abstract. Among others, Alferes et al. (1998) presented an approach for updating logic programs with sets of rules based on dynamic logic programs. We syntactically redefine dynami...
Thomas Eiter, Michael Fink, Giuliana Sabbatini, Ha...
We prove that one can express the vertex-minor relation on finite undirected graphs by formulas of monadic second-order logic (with no edge set quantification) extended with a p...
Data Warehouse logical design involves the definition of structures that enable an efficient access to information. The designer builds relational or multidimensional structures ta...