Abstract
The general context of this work is the issue of designing high-quality systems that integrate multiple data sources via a semantic layer encoded in a knowledge representation and reasoning language. We consider knowledge-based data management (KBDM) systems, which are structured in three layers: the data layer, which comprises the data sources, the knowledge (or ontological) layer, and the mappings between the two. Mappings and knowledge are expressed within the existential rule framework. One of the intrinsic difficulties in designing a KBDM is the need to understand the content of data sources. Data sources are often provided with typical queries and constraints, from which valuable information about their semantics can be drawn, as long as this information is made intelligible to KBDM designers. This motivates our core question: is it possible to translate data queries and constraints at the knowledge level while preserving their semantics?The main contributions of this thesis are the following. We extend previous work on data-to-ontology query translation with new techniques for the computation of perfect, minimally complete, or maximally sound query translations. Concerning data-to-ontology constraint translation, we define a general framework and apply it to several classes of constraints. Finally, we provide a sound and complete query rewriting operator for disjunctive existential rules and disjunctive mappings, as well as undecidability results, which are of independent interest.