Search

In this paper we introduce Eddy, a new open-source tool for the graphical editing of OWL~2 ontologies. Eddy is specifically designed for creating ontologies in Graphol, a completely visual ontology language that is equivalent to OWL~2. Thus, in Eddy ontologies are easily drawn as diagrams, rather than written as sets of formulas, as commonly happens in popular ontology design and engineering environments. This makes Eddy particularly suited for usage by people who are more familiar with diagramatic languages for conceptual modeling rather than with typical ontology formalisms, as is often required in non-academic and industrial contexts. Eddy provides intuitive functionalities for specifying Graphol diagrams, guarantees their syntactic correctness, and allows for exporting them in standard OWL 2 syntax. A user evaluation study we conducted shows that Eddy is perceived as an easy and intuitive tool for ontology specification.

In this paper we study the evolution of ontologybased 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. © 2017 Lawrence Erlbaum Associates, Inc.

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.

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.

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.