Ricerc@Sapienza

Information extraction (IE) refers to the task of turning text documents into a structured form, in order to make the information contained therein automatically processable. Ontology Mediated Information Extraction (OMIE) is a new paradigm for IE that seeks to exploit the semantic knowledge expressed in ontologies to improve query answering over unstructured data (properly raw text).

Information Extraction (IE) is the task of automatically organizing in a structured form data extracted from free text documents. In several contexts, it is often desirable that extracted data are then organized according to an ontology, which provides a formal and conceptual representation of the domain of interest. Ontologies allow for a better data interpretation, as well as for their semantic integration with other information, as in Ontology-based Data Access (OBDA), a popular declarative framework for data management where an ontology is connected to a data layer through mappings.

A significant portion of information that is nowadays collected in enterprises and organizations resides in text documents, and thus is inherently unstructured. Turning it into a structured form is the aim of information extraction (IE). Depending on the approach followed, the output of an IE process can fill forms or populate relational tables, or can be presented through an ontology.

An Ontology-based Data Access system is constituted by an ontology, namely a description of the concepts and the relations in a domain of interest, a database storing facts about the domain, and a mapping between the data and the ontology. In this paper, we consider ontologies expressed in the popular DL-Lite family of Description Logic, and we address the problem of computing explanations for answers to queries in an OBDA system, where queries are either positive, in particular conjunctive queries, or negative, i.e., negation of conjunctive queries.

Ontology-based data management (OBDM) is a powerful knowledge-oriented paradigm for managing data spread over multiple heterogeneous sources. In OBDM, the data sources of an information system are handled through the reconciled view provided by an ontology, i.e., the conceptualization of the underlying domain of interest expressed in some formal language. In any information systems where the basic knowledge resides in data sources, it is of paramount importance to specify the acceptable states of such information.

Views are a mechanisms for precomputing answers to query of particular significance. Views have a definition (the query itself) and an extension obtained by evaluating the query over the data sources. Views are used for controlling the access to data and keep data even when the original sources are not accessible anymore. In this paper we introduce views definitions in DL-LiteR ontologies as an additional form of assertions in the TBox, and we study the basic reasoning tasks involving them, including consistency, containment, disjointness, projection classification, and query answering.

In Ontology-Based Data Access (OBDA), a domain ontology is linked to the data sources of an organization in order to query, integrate and manage data through the concepts and relations of the domain of interest, thus abstracting from the structure and the implementation details of the data layer.