Gerardo I. Simari

TL;DR: This work performs an in-depth analysis of the complexity of consistent query answering under the main decidable classes of existential rules enriched with negative constraints, and focuses on one of the most prominent inconsistency-tolerant semantics, namely, the AR semantics.

TL;DR: A probabilistic extension of Datalog+ / − is developed, which uses Markov logic networks as the underlying probabilism semantics and focuses especially on scalable algorithms for answering threshold queries, which correspond to the question “what is the set of all ground atoms that are inferred from a given Probabilistic ontology with a probability of at least p?”

TL;DR: This paper develops a general framework for inconsistency management in Datalog+/- ontologies based on incision functions from belief revision, in which several query answering semantics are characterized as special cases.

TL;DR: This work presents several plausible candidate dirtiness measures from the literature and identifies which of these satisfy the authors' axioms and which do not, and defines a new dirtiness measure which satisfies all of the axiomatic.

TL;DR: The syntax and semantics of -programs are presented and a naive algorithm to solve the MPW problem using the linear program formulation commonly used for PLPs is shown and a “binary” algorithm that applies a binary search style heuristic in conjunction with the Naive algorithms to quickly find worlds that may not be “most probable.”