In this paper we study Controlled Query Evaluation (CQE), a declarative approach to privacy-preserving query answering over databases, knowledge bases, and ontologies. CQE is based on the notion of censor, which defines the answers to each query posed to the data/knowledge base. We investigate both semantic and computational properties of CQE in the context of OWL ontologies, and specifically in the description logic DL-LiteR, which underpins the OWL 2 QL profile. In our analysis, we focus on semantics of CQE based on censors (called optimal GA censors) that enjoy the so-called indistinguishability property, analyzing the trade-off between maximizing the amount of data disclosed by query answers and minimizing the computational cost of privacy-preserving query answering. We first study the data complexity of skeptical entailment of unions of conjunctive queries under all the optimal GA censors, showing that the computational cost of query answering in this setting is intractable. To overcome this computational issue, we then define a different semantics for CQE centered around the notion of intersection of all the optimal GA censors. We show that query answering over OWL 2 QL ontologies under the new intersection-based semantics for CQE enjoys tractability and is first-order rewritable, i.e. amenable to be implemented through SQL query rewriting techniques and the use of standard relational database systems; on the other hand, this approach shows limitations in terms of amount of data disclosed. To improve this aspect, we add preferences between ontology predicates to the CQE framework, and identify a semantics under which query answering over OWL 2 QL ontologies maintains the same computational properties of the intersection-based approach without preferences.

In this paper, we revisit the problem of entity resolution and propose a novel, logical framework, LACE, which mixes declarative and procedural elements to achieve a number of desirable properties. Our approach is fundamentally declarative in nature: it utilizes hard and soft rules to specify conditions under which pairs of entity references must or may be merged, together with denial constraints that enforce consistency of the resulting instance. Importantly, however, rule bodies are evaluated on the instance resulting from applying the already 'derived' merges. It is the dynamic nature of our semantics that enables us to capture collective entity resolution scenarios, where merges can trigger further merges, while at the same time ensuring that every merge can be justified. As the denial constraints restrict which merges can be performed together, we obtain a space of (maximal) solutions, from which we can naturally define notions of certain and possible merges and query answers. We explore the computational properties of our framework and determine the precise computational complexity of the relevant decision problems. Furthermore, as a first step towards implementing our approach, we demonstrate how we can encode the various reasoning tasks using answer set programming.

Given an input dataset (i.e., a set of tuples), query definability in Ontology-based Data Management (OBDM) amounts to finding a query over the ontology whose certain answers coincide with the tuples in the given dataset. We refer to such a query as a characterization of the dataset with respect to the OBDM system. Our first contribution is to propose approximations of perfect characterizations in terms of recall (complete characterizations) and precision (sound characterizations). A second contribution is to present a thorough complexity analysis of three computational problems, namely verification (check whether a given query is a perfect, or an approximated characterization of a given dataset), existence (check whether a perfect, or a best approximated characterization of a given dataset exists), and computation (compute a perfect, or best approximated characterization of a given dataset).