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.

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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.

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.

While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities.

The proceedings contain 10 papers.

In the context of the Description Logic DL-Liteℛ≠, i.e., DL-Liteℛ without UNA and with inequality axioms, we address the problem of adding to unions of conjunctive queries (UCQs) one of the simplest forms of negation, namely, inequality. It is well known that answering conjunctive queries with unrestricted inequalities over DL-Liteℛ ontologies is in general undecidable. Therefore, we explore two strategies for recovering decidability, and, hopefully, tractability.

OWL2QL is the profile of OWL2 targeted to Ontology-Based Data Access (OBDA) scenarios, where large amount of data are to be accessed, and thus answering conjunctive queries over data is the main task. However, this task is quite restrained wrt the classical KR Ask-and-Tell framework based on query- ing the whole theory, not only facts (data). If we use SPARQL as query language, we get much closer to this ideal.

Cultural Heritage is a wide concept. It's what remains of the past generations Cultural Heritage includes tangible culture (such as buildings, monuments, landscapes, books, works of art and artifacts), intangible culture (such as folklore, music, traditions, language and knowledge) and natural heritage (including culturally significant landscapes, and biodiversity). A good preservation, restauration and valorization of Cultural Heritage embraces tangible and intangible culture, actually not evaluated in an holistic way.