Valerio Santarelli

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.

Effective Computation of Maximal Sound Approximations of Description Logic Ontologies

We study the problem of approximating Description Logic (DL) ontologies specified in a source language

Graph-based ontology classification in OWL 2 QL

\mathcal{L}_S

Mapping Repair in Ontology-based Data Access Evolving Systems

in terms of a less expressive target language

From OWL to DL − Lite through efficient ontology approximation

\mathcal{L}_T

Semantic Analysis of R2RML Mappings for Ontology-Based Data Access

. This problem is getting very relevant in practice: e.g., approximation is often needed in ontology-based data access systems, which are able to deal with ontology languages of a limited expressiveness. We first provide a general, parametric, and semantically well-founded definition of maximal sound approximation of a DL ontology. Then, we present an algorithm that is able to effectively compute two different notions of maximal sound approximation according to the above parametric semantics when the source ontology language is OWL 2 and the target ontology language is OWL 2 QL. Finally, we experiment the above algorithm by computing the two OWL 2 QL approximations of a large set of existing OWL 2 ontologies. The experimental results allow us both to evaluate the effectiveness of the proposed notions of approximation and to compare the two different notions of approximation in real cases.

Web Reasoning and Rule Systems

Ontology classification is the reasoning service that computes all subsumption relationships inferred in an ontology between concept, role, and attribute names in the ontology signature. OWL 2 QL is a tractable profile of OWL 2 for which ontology classification is polynomial in the size of the ontology TBox. However, to date, no efficient methods and implementations specifically tailored to OWL 2 QL ontologies have been developed. In this paper, we provide a new algorithm for ontology classification in OWL 2 QL, which is based on the idea of encoding the ontology TBox into a directed graph and reducing core reasoning to computation of the transitive closure of the graph. We have implemented the algorithm in the QuOnto reasoner and extensively evaluated it over very large ontologies. Our experiments show that QuOnto outperforms various popular reasoners in classification of OWL 2 QL ontologies.

Semantic analysis of R2RML mappings for ontology-based data access

In this paper we study the evolution of ontology based data access (OBDA) specifications, and focus on the case in which the ontology and/or the data source schema change, which may require a modification to the mapping between them to preserve both consistency and knowledge. Our approach is based on the idea of repairing the mapping according to the usual principle of minimal change and on a recent, mapping-based notion of consistency of the specification. We define and analyze two notions of mapping repair under ontology and source schema update. We then present a set of results on the complexity of query answering in the above framework, when the ontology is expressed in DL-LiteR.

Towards efficient and practical solutions for ontology-based data management

Ontologies provide a conceptualization of a domain of interest which can be used for different objectives, such as for providing a formal description of the domain of interest for documentation purposes, or for providing a mechanism for reasoning upon the domain. For instance, they are the core element of the Ontology-Based Data Access (OBDA) [3,8] paradigm, in which the ontology is utilized as a conceptual view, allowing user access to the underlying data sources. With the aim to use an ontology as a formal description of the domain of interest, the use of expressive languages proves to be useful. If instead the goal is to use the ontology for reasoning tasks which require low computational complexity, the high expressivity of the language used to model the ontology may be a hindrance. In this scenario, the approximation of ontologies expressed in very expressive languages through ontologies expressed in languages which keep the computational complexity of the reasoning tasks low is pivotal.

Optique 1.0: semantic access to big data: the case of Norwegian petroleum directorate's factpages

Ontology-based data access (OBDA) deals with the problem of accessing autonomous data sources through a shared, virtual ontology, and declarative mappings connecting the data sources to the ontology. The W3C standard R2RML allows for mapping relational data sources to RDFS/OWL ontologies. In this paper, we present algorithms for the semantic analysis of R2RML mappings in the OBDA setting, when the ontology is expressed in OWL 2 QL. The focus of such algorithms is to identify the main semantical anomalies (inconsistency and redundancy) of a mapping specification with respect to the ontology and/or the data sources. Such algorithms have been implemented in the mapping analysis tool developed within the Optique European project. We also report on the experiments conducted within the Optique project use cases.