As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important di#erences between database schemas and ontologies. The di#erences stem from di#erent usage paradigms, the presence of explicit semantics, and di#erent knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These di#erences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is di#erent. We must develop automatic techniques for finding similarities and di#erences between versions.

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A model is a formal description of a complex application artifact, such as a database schema, an application interface, a UML model, an ontology, or a message format. The problem of merging such models lies at the core of many meta data applications, such as view integration, mediated schema creation for data integration, and ontology merging. This paper examines the problem of merging two models given correspondences between them. It presents requirements for conducting a merge and a specific algorithm that subsumes previous work.

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.

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.

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The difficulty inherent in schema matching has led to the development of several generic match algorithms. This paper describes how we adapted general approaches to the specific task of aligning two ontologies of human anatomy, the Foundational Model of Anatomy and the GALEN Common Reference Model. Our approach consists of three phases: lexical, structural and hierarchical, which leverage different aspects of the ontologies as they are represented in a generic meta-model. Lexical matching identifies concepts with similar names. Structural matching identifies concepts whose neighbors are similar. Finally, hierarchical matching identifies concepts with similar descendants. We conclude by reporting on the lessons we learned.

A common language, or terminology, for representing what clinicians have said and done is an important requirement for individual clinical systems, and it is a prerequisite for integrating disparate applications in a distributed telematic healthcare environment. Formal representations based on description logics or closely related formalisms are increasingly used for representing medical terminologies. GALEN's experience in using one such formalism raises two major issues: . How to make ontologies based on description logics easy to use and understand for both clinicians and applications developers; . What features are required of the ontology and description logic if they are to achieve their aims. Based on our experience we put forward four contentions: two relating to each of these two issues: . That natural language generation is essential to make a description logic based ontology accessible to users; . That the description logic based ontology should be treated as an `assembl...

The elicitation of the ontology -- i.e. the objects of a domain -- is a key issue of conceptual modelling and therefore of knowledge acquisition. The Conceptual Graph Theory provides a knowledge representation formalism to be used in knowledgebased systems with an explicit "type lattice" to account for the ontology. Since knowledge is in most AI applications non formal, it has to be normalized to ensure that the formal exploitation of its representation conforms to its meaning in the domain. Noting the intensional nature of types, which reflect the essences of the objects they denote, this normalization relies on a commitment on type definitions by necessary and sufficient conditions at the knowledge level. Our claim is that the taxonomic structure that accounts for the intensional nature of the ontology can be nothing but a tree, precluding tangled taxonomies. From this starting point, we derive methodological principles to constrain the acquisition of the "type tree", thus...

this paper, we examine some methodological and theoretical principles to enforce this conformity. These principles result from our experience in Menelas, a medical language understanding project.