In this paper we present a new semantics, called Local Models Semantics, and use it to provide a foundation to reasoning with contexts. This semantics captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context (of reasoning); however (ii) there is compatibility among the reasoning performed in different contexts. We validate our semantics by formalizing two important forms of contextual reasoning: reasoning with viewpoints and reasoning about belief.

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party's subjective view of a domain. In this paper we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.

MultiLanguage systems (ML systems) are formal systems allowing the use of multiple distinct logical languages. In this paper we introduce a class of ML systems which use a hierarchy of first order languages, each language containing names for the language below, and propose them as an alternative to modal logics. The motivations of our proposal are technical, epistemological and implementational. From a technical point of view, we prove, among other things, that the set of theorems of the most common modal logics can be embedded (under the obvious bijective mapping between a modal and a first order language) into that of the corresponding ML systems. Moreover, we show that ML systems have properties not holding for modal logics and argue that these properties are justified by our intuitions. This claim is motivated by the study of how ML systems can be used in the representation of beliefs (more generally, propositional attitudes) and provability, two areas where modal logics have been extensively used. Finally, from an implementation point of view, we argue that ML systems resemble closely the current practice in the computer representation of propositional attitudes and metatheoretic theorem proving.

It is widely agreed on that most cognitive processes are contextual in the sense that they depend on the environment, or context, inside which they are carried on. Even concentrating on the issue of contextuality in reasoning, many different notions of context can be found in the Artificial Intelligence literature. Our intuition is that reasoning is usually performed on a subset of the global knowledge base. The notion of context is used as a means of formalizing this idea of localization. Roughly speaking, we take a context to be the set of facts used locally to prove a given goal plus the inference routines used to reason about them (which in general are different for different sets of facts). Our perspective is similar to that proposed in [McC87, McC91]. The goal of this paper is to propose an epistemologically adequate theory of reasoning with contexts. The emphasis is on motivations and intuitions, rather than on technicalities. The two basic definitions are reported i...

We propose a new data model intended for peer-to-peer (P2P) databases. The model

We view match as an operator that takes two graph-like structures (e.g., classifications, XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each other. Semantic matching is based on two ideas: (i) we discover mappings by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (concepts, not labels) which is codified in the elements and the structures of schemas. In this paper we present basic and optimized algorithms for semantic matching, and we discuss their implementation within the S-Match system. We evaluate S-Match against three state of the art matching systems, thereby justifying empirically the strength of our approach.

The goal of this paper is to provide a formalization of monotonic belief and belief about belief in a multiagent environment. We distinguish between ideal beliefs, i.e., those beliefs which satisfy certain "idealized" properties which are unlikely to be possessed by real agents, and real beliefs. Our formalization is based on a set-theoretic specification of beliefs and, then, on the definition of the appropriate constructors which present the sets identified. This allows us to provide a uniform and taxonomic characterization of the possible ways in which ideal and real beliefs can arise. We provide intuitions about the conceptual importance of the cases analyzed by proving and discussing some equivalence results with some important modal systems modeling various forms of (non) logical omniscience. 1 Introduction We are interested in the formalization of monotonic belief and belief about belief in a multiagent environment. Here, we restrict ourselves to the propositional case. We dis...

We propose a framework, called OM pairs, for the formalization of metareasoning. OM pairs allow us to generate deductively the object theory and/or the meta theory. This is done by imposing, via appropriate reflection rules, the relation we want to hold between the object theory and the meta theory. In this paper we concentrate on the proof theory of OM pairs. We study them from three different points of view: we compare the strength of the object and meta theories generated by different OM pairs; for each OM pair we study the precise form of the object theory and meta theory; and, finally, we study three important case studies.

In the last decade the concept of context has been extensively exploited in many research areas, e.g., distributed artificial intelligence, multi agent systems, distributed databases, information integration, cognitive science, and epistemology. Three alternative approaches to the formalization of the notion of context have been proposed: Giunchiglia and Serafini's Multi Language Systems (ML systems), McCarthy's modal logics of contexts, and Gabbay's Labelled Deductive Systems. Previous papers have argued in favor of ML systems with respect to the other approaches. Our aim in this paper is to support these arguments from a theoretical perspective. We provide a very general definition of ML systems, which covers all the ML systems used in the literature, and we develop a proof theory for an important subclass of them: the MR systems. We prove various important results; among other things, we prove a normal form theorem, the sub-formula property, and the decidability of an important instance of the class of the MR systems. The paper concludes with a detailed comparison among the alternative approaches.

The problem of context has a long tradition in different areas of artificial intelligence (AI). However, the issue of formalizing context has become a widely discussed issue only the late 80s, when J. McCarthy argued that formalizing context was a crucial step toward the solution of the problem of generality. Since then, two main formalizations have been proposed in AI: Propositional Logic of Context (PLC), by Buvac and Mason; and Local Models Semantics/MultiContext Systems (LMS/MCS), by Ghidini and Giunchiglia / Giunchiglia and Serafini.