Ricerc@Sapienza

Ontologies are an important resource for knowledge representation. Their structure can be complex due to role relations between several concepts, distinct attributes, and different instances. In this paper, we discuss a Visual Analytics solution, relying on the use of multiple coordinated views for exploring different ontology aspects and a novel use of the degree of interest (DoI) suppression technique to reduce the complexity of the ontology visual representation.

Ontologie als transzendentale Anthropologie: Kant und das Problem der Metaphysik um 1775

Open-Source INTelligence (OSINT) is intelligence based on publicly available sources, such as news sites, blogs, forums, etc. The Web is the primary source of information, but once data are crawled from it, they need to be interpreted and structured. Ontologies may play a crucial role in this process, but due to the vast amount of documents available, automatic mechanisms for their population starting from the crawled text are needed.

Semantic technologies combine knowledge representation techniques with artificial intelligence in order to achieve a more effective management of enterprise knowledge bases, thanks to the separation of the conceptual level of the applications from the logical and physical ones, and to the automatic reasoning services they deploy for data access and control.

The quantitative evaluation of research is currently carried out by means of indicators calculated on data extracted and integrated by analysts who elaborate them by creating illustrative tables and plots of results. In this paper we propose a new approach which is able to move forward, from indicators’ development to performance model’s development. It combines the advantages of the Ontology-based data Access (OBDA) integration with the flexibility and robustness of a Visual Analytics (VA) environment. A detailed description of such an approach is presented in the paper.

In Controlled Query Evaluation (CQE) confidential data are protected through a declarative policy and a (optimal) censor, which guarantees that answers to queries are maximized without disclosing secrets. In this paper we consider CQE over Description Logic ontologies and study query answering over all optimal censors. We establish data complexity of the problem for ontologies specified in DL-LiteR and for variants of the censor language, which is the language used by the censor to enforce the policy.

The issue of cooperation, integration, and coordination between information peers has been addressed over the years both in the context of the Semantic Web and in several other networked environments, including data integration, Peer-to-Peer and Grid computing, service-oriented computing, distributed agent systems, and collaborative data sharing. One of the main problems arising in such contexts is how to exploit the mappings between peers in order to answer queries posed to one peer.

It is well-known that answering conjunctive queries with inequalities (CQ≠s) over DL-LiteR ontologies is in general undecidable. In this paper we consider the subclass of CQ≠s, called CQ≠,bs, where inequalities involve only distinguished variables or individuals. In particular, we tackle the problem of answering C≠,bs and unions thereof (UC≠,bs s) over DL-LiteR≠ ontologies, where DL-LiteR≠ corresponds to DL-LiteR without the Unique Name Assumption, and with the possibility of asserting inequalities between individuals, as in OWL 2 QL.

We study the problem of designing reverse engineering techniques for associating semantic descriptions to existing data services. We base our proposal on the Ontology-Based Data Access paradigm, where a domain ontology is used to provide a semantic layer mapped to the data sources of an organization. The basic idea is to perform the reverse engineering of a data service, expressed a query over the data sources, by deriving a query over the ontology that explains the semantics of the data service in terms of the element of the ontology.

The rise of data mining and machine learning use in many applications has brought new challenges related to classification. Here, we deal with the following challenge: how to interpret and understand the reason behind a classifier's prediction.