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.

Large-scale ontologies are becoming an essential component of many applications including standard search (such as Yahoo and Lycos), ecommerce (such as Amazon and eBay), configuration (such as Dell and PC-Order), and government intelligence (such as DARPA’s High Performance Knowledge Base (HPKB) program). The ontologies are becoming so large that it is not uncommon for distributed teams of people with broad ranges of training to be in charge of the ontology development, design, and maintenance. Standard ontologies (such as UNSPSC) are emerging as well which need to be integrated into large application ontologies, sometimes by people who do not have much training in knowledge representation. This process has generated needs for tools that support broad ranges of users in (1) merging of ontological terms from varied sources, (2) diagnosis of coverage and correctness of ontologies, and (3) maintaining ontologies over time. In this paper, we present a new merging and diagnostic ontology environment called Chimaera, which was developed to address these issues in the context of HPKB. We also report on some initial tests of its effectiveness in merging tasks. 1

Abstract. Ontologies now play an important role for enabling the semantic web. They provide a source of precisely defined terms e.g. for knowledge-intensive applications. The terms are used for concise communication across people and applications. Typically the development of ontologies involves collaborative efforts of multiple persons. OntoEdit is an ontology editor that integrates numerous aspects of ontology engineering. This paper focuses on collaborative development of ontologies with OntoEdit which is guided by a comprehensive methodology. 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.

The word integration has been used with different meanings in the ontology field. This article aims at clarifying the meaning of the word "integration" and presenting some of the relevant work done in integration. We identify three meanings of ontology "integration": when building a new ontology reusing (by assembling, extending, specializing or adapting) other ontologies already available; when building an ontology by merging several ontologies into a single one that unifies all of them; when building an application using one or more ontologies. We discuss the different meanings of "integration", identify the main characteristics of the three different processes and propose three words to distinguish among those meanings: integration, merge and use.

This paper describes an integrated acquisition interface that includes several techniques previously developed to support users in various ways as they add new knowledge to an intelligent system. As a result of this integration, the individual techniques can take better advantage of the context in which they are invoked and provide stronger guidance to users. We describe the current implementation using examples from a travel planning domain, and demonstrate how users can add complex knowledge to the system.

This papers presents an approach for modeling largescale ontologies. We extend well-established methods for modeling concepts and relations by transportable methods for modeling ontological axioms. The gist of our approach lies in the way we treat the majority of axioms. They are categorized into different types and specified as complex objects that refer to concepts and relations. Considering language and system particularities, this first layer of representation is then translated into the target representation language. This two-layer approach benefits engineering, because the intended meaning of axioms is captured by the categorization of axioms. Classified object representations allow for versatile access to and manipulations of axioms via graphical user interfaces.

The paper presents a review of the ONIONS project. ONIONS is committed to developing a large­scale ontology library for medical terminology. The developed methodology exploits a description logic­based design for the modules in the library and makes extended use of generic theories, thus creating a stratification of the modules. Terminological knowledge is acquired by conceptual analysis and ontology integration over a set of authoritative sources. After addressing general issues about conceptual analysis and integration, the methodology is briefly described. The central part of the article presents the investigation we have made on the 476,000 medical concepts singled out by the National Library of Medicine as the Metathesaurus^TM in the UMLS project. This is followed by several case studies concerning lexical polysemy, the interface between ontologies and lexicon, and other special problems encountered in the specification of the ontologies. A section describing the current structure of the library and the generic theories reused is provided. Current results of our research include the integration of some top­level ontologies in the ON9.2 ontology library, and the formalization of the terminological knowledge in the UMLS Metathesaurus.

s. The Plinius ontology was developed to support the translation of natural-language sentences into expressions in a knowledge representation language [31]. Those design decisions taken during the development of the ontology which appeared to be domain-independent have been proposed as general ontology development principles. These are: (1) conflicting assertions about the same entity can be more readily discovered if the concepts are defined as fully as possible. (2) pre-existing formal theories are taken as given and a domain ontology does not specify the semantics of logical constants. (3) an ontology should be independent of any particular knowledge representation language. (4) the principle of the conceptual construction kit states that an ontology consists of primitives concepts and construction rules that allow the definition of all other concepts in terms of these primitives. (5) a bottom-up approach is taken in order that the ontology exhibits sufficient completeness for the...

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