Lorenzo Lepore

Mastro studio: managing ontology-based data access applications

Ontology-based data access (OBDA) is a novel paradigm for accessing large data repositories through an ontology, that is a formal description of a domain of interest. Supporting the management of OBDA applications poses new challenges, as it requires to provide effective tools for (i) allowing both expert and non-expert users to analyze the OBDA specification, (ii) collaboratively documenting the ontology, (iii) exploiting OBDA services, such as query answering and automated reasoning over ontologies, e.g., to support data quality check, and (iv) tuning the OBDA application towards optimized performances. To fulfill these challenges, we have built a novel system, called MASTRO STUDIO, based on a tool for automated reasoning over ontologies, enhanced with a suite of tools and optimization facilities for managing OBDA applications. To show the effectiveness of MASTRO STUDIO, we demonstrate its usage in one OBDA application developed in collaboration with the Italian Ministry of Economy and Finance.

Making Metaquerying Practical for Hi(DL − LiteR) Knowledge Bases

Hi(DL − Lite R ) is a higher-order Description Logic obtained from DL − Lite R by adding metamodeling features, and is equipped with a query language that is able to express higher-order queries. We investigate the problem of answering a particular class of such queries, called instance higher-order queries, posed over Hi(DL − Lite R ) knowledge bases (KBs). The only existing algorithm for this problem is based on the idea of reducing the evaluation of a higher-order query q over a Hi(DL − Lite R ) KB to the evaluation of a union of first-order queries over a DL − Lite R KB, built from q by instantiating its metavariables in all possible ways. Although of polynomial time complexity with respect to the size of the KB, this algorithm turns out to be inefficient in practice. In this paper we present a new algorithm, called Smart Binding Planner (SBP), that compiles Q into a program, that issues a sequence of first-order conjunctive queries, where each query has the goal of providing the bindings for metavariables of the next ones, and the last one completes the process by computing the answers to Q. We also illustrate some experiments showing that, in practice, SBP is significantly more efficient than the previous approach.