Research on ontology is becoming increasingly widespread in the computer science community, and its importance is being recognized in a multiplicity of research fields and application areas, including knowledge engineering, database design and integration, information retrieval and extraction. We shall use the generic term information systems, in its broadest sense, to collectively refer to these application perspectives. We argue in this paper that so-called ontologies present their own methodological and architectural peculiarities: on the methodological side, their main peculiarity is the adoption of a highly interdisciplinary approach, while on the architectural side the most interesting aspect is the centrality of the role they can play in an information system, leading to the perspective of ontology-driven information systems.

Taxonomies are an important part of conceptual modeling. They provide substantial structural information, and are typically the key elements in integration efforts, however there has been little guidance as to what makes a proper taxonomy. We have adopted several notions from the philosophical practice of formal ontology, and adapted them for use in information systems. These tools, identity, essence, unity, and dependence, provide a solid logical framework within which the properties that form a taxonomy can be analyzed. This analysis helps make intended meaning more explicit, improving human understanding and reducing the cost of integration.

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent, entity classes.

The vision of a Semantic Web has recently drawn considerable attention, both from academia and industry. Description logics are often named as one of the tools that can support the Semantic Web and thus help to make this vision reality.

Abstract. In this paper we present a fuzzy version of SHOIN (D), the corresponding

The purpose of this paper is to explore some semantic problems related to the use of linguistic ontologies in information systems, and to suggest some organizing principles aimed t o solve such problems. The taxonomic structure of current ontologies is unfortunately quite complicated and hard to understand, especially for what concerns the upper levels. I will focus here on the problem of ISA overloading, which I believe is the main responsible of these difficulties. To this purpose, I will carefully analyze the ontological nature of the categories used in current upper-level structures, considering the necessity of splitting them according to more subtle distinctions or the opportunity of excluding them because of their limited organizational role.

We present a logical theory of space where only tridimensional regions are assumed in the domain. Three distinct primitives are used to describe their mereological, topological and morphological properties: mereology is described by a parthood relation satisfying the axioms of Closed Extensional Mereology; topology is described by means of a "simple region" predicate, by which a relation of “strong connection ” between regions having at least a surface in common is defined; morphology is described by means of a "congruence " primitive, whose axioms exploit Tarski's analogy between points and spheres. 1

The task of information extraction can be seen as a problem of semantic matching between a user-defined template and a piece of information written in natural language. To this purpose, the ontological assumptions of the template need to be suitably specified, and compared with the ontological implications of the text. So-called "ontologies", consisting of theories of various kinds expressing the meaning of shared vocabularies, begin to be used for this task. This paper addresses the theoretical issues related to the design and use of such ontologies for purposes of information retrieval and extraction. After a discussion on the nature of semantic matching within a model-theoretical framework, we introduce the subject of Formal Ontology, showing how the notions of parthood, integrity, identity, and dependence can be of help in understanding, organizing and formalizing fundamental ontological distinctions. We present then some basic principles for ontology design, and we illustrate a preliminary proposal for a top-level ontology develped according to such principles. As a concrete example of ontology-based information retrieval, we finally report an ongoing experience of use of a large linguistic ontology for the retrieval of object-oriented software components.

In their paper on "Using Explicit Ontologies in KBS Development", van Heijst and colleagues seem to take for granted Bylander and Chandrasekaran 's hypothesis on the strong dependence of knowledge represesentation on the nature and the inference strategy of the problem at hand, the socalled interaction problem: Representing knowledge for the purpose of solving some problem is strongly affected by the nature of the problem and the inference strategy to be applied to the problem. [Bylander and Chandrasekaran 1988] The fact that the van Heijst and colleagues don't attempt to explore in detail the arguments sustaining this hypothesis is particularly puzzling, since they admit that it contradicts one of the main assumptions of their well-known KADS approach [Schreiber et al. 1993], namely the separation between domain knowledge and problem-solving knowledge. They report two reasons brought by Bylande

The growth of the Internet has revitalised research on the integration of heterogeneous information sources. Integration efforts face a trade off between interoperability and heterogeneity. Important integration obstacles arise from the differences in the underlying ontologies of the various sources. In this paper we investigate the impediments to integration, focussing on ontologies. In particular, we present a classification of ontology mismatches (distinguishing conceptualisation mismatches and explication mismatches as its main categories), and discuss how each of the mismatch types can be dealt with. The idea is that knowing which ontology mismatches are difficult to deal with may contribute to finding a balance between heterogeneity and interoperability.