We study the complexity of exchanging probabilistic data between ontology-based probabilistic databases. We consider the Datalog+/- family of languages as ontology and ontology mapping languages, and we assume different compact encodings of the probabilities of the probabilistic source databases via Boolean events. We provide an extensive complexity analysis of the problem of deciding the existence of a probabilistic (universal) solution for a given probabilistic source database relative to a (probabilistic) data exchange problem for the different languages considered.

We discuss in this paper, from a pragmatic and operational point of view, the need of a clear differentiation between functional and semantic “roles.” In the first case, according to the linguistic and computational linguistics tradition, roles are seen as relations linking a semantic predicate to its arguments. In the second, in conformity with the ontological and Semantic Web practice, roles are equated to ordinary concepts to be inserted into a standard ontology. As we will show here, the two notions can successfully co-exist in the framework of a high level conceptual modeling language.

Narratives can be defined informally as a “spatio-temporally bounded stream of elementary events”. To make this sort of definition more computationally useful we introduce, firstly, some pragmatic criteria for recognizing highly ambiguous entities like the “elementary events” and for linking these events together into complete narratives. We raise then the problem of how to concretely represent elementary events and narratives in computer-suitable form. We introduce then the main characteristics of a language, NKRL (Narrative Knowledge Representation Language), expressly specified and implemented for dealing with (non-fictional) narratives and temporal information. We conclude by showing briefly how this language can be used for questioning and for particularly complex inference operations.