Learning in Description Logics (DLs) has been paid increasing attention over the last decade. Several and diverse approaches have been proposed which however share the common feature of extending and adapting previous work in Concept Learning to the novel representation framework of DLs. In this paper we present a declarative modeling language for Concept Learning in DLs which relies on recent results in the fields of Knowledge Representation and Machine Learning. Based on second-order DLs, it allows for modeling Concept Learning problems as constructive DL reasoning tasks where the construction of the solution to the problem may be subject to optimality criteria.

Among the inferences studied in Description Logics (DLs), induction has been paid increasing attention over the last decade. Indeed, useful non-standard reasoning tasks can be based on the inductive inference. Among them, Concept Learning is about the automated induction of a description for a given concept starting from classified instances of the concept. In this paper we present a formal characterization of Concept Learning in DLs which relies on recent results in Knowledge Representation and Machine Learning.

Fuzzy Description Logics (DLs) are logics that allow to deal with structured vague knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing the evolution of fuzzy ontologies has received very little attention so far. We describe here a logic-based computational method for the automated induction of fuzzy ontology axioms which follows the machine learning approach of Inductive Logic Programming. The potential usefulness of the method is illustrated by means of an example taken from the tourism application domain.

Onto-Relational Learning is an extension of Relational Learning aimed at accounting for ontologies in a clear, well-founded and elegant manner. The system AL-QuIn supports a variant of the frequent pattern discovery task by following the Onto-Relational Learning approach. It takes taxonomic ontologies into account during the discovery process and produces descriptions of a given relational database at multiple granularity levels. The functionalities of the system are illustrated by means of examples taken from a Semantic Web Mining case study concerning the analysis of relational data extracted from the on-line CIA World Fact Book.

Integrated Tourism can be defined as the kind of tourism which is explicitly linked to the localities in which it takes place and, in practical terms, has clear connections with local resources, activities, products, production and service industries, and a participatory local community. In this paper we report our experience in applying Artificial Intelligence techniques to Integrated Tourism planning in urban areas. In particular, we have modeled a domain ontology for Integrated Tourism and developed an Information Extraction tool for populating the ontology with data automatically retrieved from the Web. Also, we have defined several Semantic Web Services on top of the ontology and applied a Machine Learning tool to better adapt the automated composition of these services to user demands. Use cases of the resulting service infrastructure are illustrated for the Apulia Region, Italy.

Fuzzy Description Logics (DLs) are logics that allow to deal with vague structured knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing fuzzy ontologies has received very little attention so far. We report here our preliminary investigation on this issue by describing a method for inducing inclusion axioms in a fuzzy DL-Lite like DL.

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e., the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of Knowledge Representation (KR) aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework DL+LOG. We illustrate the application scenarios by means of examples.

With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with an emphasis on the Italian research.

This chapter contains a reference selection of Italian contributions in the intersection of Logic Programming (LP) with databases and the (Semantic) Web. More precisely, we will survey the main contributions on deductive databases such as the coupling of Prolog systems and database systems, evaluation and optimization techniques, Datalog extensions for expressing nondeterministic and aggregate queries, and active rules and their relation to deductive rules. Also we will illustrate solutions employing LP for querying the Web, manipulating Web pages, representing knowledge in the Semantic Web and learning Semantic Web ontologies and rules.

The integration of fuzzy sets in ontologies for the Semantic Web can be achieved in different ways. In most cases, fuzzy sets are defined by hand or with some heuristic procedure that does not take into account the distribution of available data. In this paper, we describe a method for introducing a granular view of data within an OWL ontology.

Fuzzy Description Logics (DLs) are logics that allow to deal with vague structured knowledge. Although a relatively important amount of work has been carried out in the last years concerning the use of fuzzy DLs as ontology languages, the problem of automatically managing fuzzy ontologies has received no attention so far. We report here our preliminary investigation on this issue by describing a method for inducing inclusion axioms in a fuzzy DL-Lite like DL.