Abstract. We present a novel combination of disjunctive logic programs under the answer set semantics with description logics for the Semantic Web. The combination is based on a well-balanced interface between disjunctive logic programs and description logics, which guarantees the decidability of the resulting formalism without assuming syntactic restrictions. We show that the new formalism has very nice semantic properties. In particular, it faithfully extends both disjunctive programs and description logics. Furthermore, we describe algorithms for reasoning in the new formalism, and we give a precise picture of its computational complexity. We also provide a special case with polynomial data complexity. 1

The purpose of this tutorial is to get the audience familiar with the Answer Set Programming (ASP) Paradigm in the perspective of its fruitful usage for Semantic Web applications. ASP is a declarative programming paradigm with its roots in Knowledge Representation and Logic Programming. Systems and languages based on ASP are ready for tackling many of the challenges the Semantic Web offers, and in particular, are good candidates for solving a variety of issues which have been delegated to the Rule/Logic Layers in the Semantic Web vision. ASP systems are scalable, allow to mix monotonic with nonmonotonic reasoning, permit to combine rules with ontologies, and can interface external reasoners. Moreover, ASP is especially tailored at solving configuration and matchmaking problems involving reasoning with preferences by featuring easy to use, fully declarative soft & hard constraint specification languages. We introduce the attendees to the ASP basics and its principal extensions tailored at Semantic Web applications. We discuss the current impact of Answer Set Programming in the Semantic Web Area and possible future directions. Applications and exercises are presented. The attendees will practice through an online interface using one of the state-of-the-art ASP solvers and some of its extensions.

Abstract. In this paper we present O-DEVICE, a deductive object-oriented knowledge base system for reasoning over OWL documents. O-DEVICE imports OWL documents into the CLIPS production rule system by transforming OWL ontologies into an object-oriented schema of the CLIPS Object-Oriented Language (COOL) and instances of OWL classes into COOL objects. The purpose of this transformation is to be able to use a deductive object-oriented rule language for reasoning about OWL data. The O-DEVICE data model for OWL ontologies maps classes to classes, resources to objects, property types to class slot (or attribute) definitions and encapsulates resource properties inside resource objects, as traditional OO attributes (or slots). In this way, when accessing properties of a single resource, few joins are required. O-DEVICE is an extension of a previous system, called R-DEVICE, which effectively maps RDF Schema and data into COOL objects and then reasons over RDF data using a deductive object-oriented rule language. 1

Abstract. This paper reports on the implementation of a rule system, called O-DEVICE, for reasoning about OWL instances using deductive rules. O-DEVICE exploits the rule language of the CLIPS production rule system and transforms OWL ontologies into an object-oriented schema of COOL. During the transformation procedure, OWL classes are mapped to COOL classes, OWL properties to class slots and OWL instances to COOL objects. The purpose of this transformation is twofold: a) to exploit the advantages of the object-oriented representation and access all the properties of instances in one step, since properties are encapsulated inside resource objects; b) to be able to use a deductive object-oriented rule language for querying and creating maintainable views of OWL instances, which operates over the object-oriented schema of CLIPS, and c) to answer queries faster, since the implied relationships due to the rich OWL semantics have been pre-computed. The deductive rules are compiled into CLIPS production rules. The rich open-world semantics of OWL are partly handled by the incremental transformation procedure and partly by the rule compilation procedure. 1.