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.

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.

Most enterprises agree that knowledge is an essential asset for success and survival on a increasingly competitive and global market. This awareness is one of the main reasons for the exponential growth of knowledge management in the past decade. Our approach to knowledge management is based on ontologies, and makes knowledge assets intelligently accessible to people in organizations. Most company-vital knowledge resides in the heads of people, and thus successful knowledge management does not only consider technical aspects, but also social ones. In this paper, we describe an approach to intelligent knowledge management that explicitly takes into account the social issues involved. The proof of concept is given by a large-scale initiative involving knowledge management of a virtual organization.

Interoperability and integration of data sources are becoming ever more important issues as both, the amount of data and the number of data producers are growing. Interoperability not only has to resolve the differences in data structures, it also has to deal with semantic heterogeneity. Semantics refer to the meaning of data in contrast to syntax, which only defines the structure of the schema items (e.g., classes and attributes). We focus on the part of semantics related to the meanings of the terms used as identifiers in schema definitions. This paper presents an approach to integrate schemas from different communities, where each such community is using its own ontology. The approach is based on merging ontologies based on similarity relations among concepts of different ontologies. We present formal definitions of similarity relations based on intensional definitions and conclude the extensional consequences. The process of merging ontologies based on the detected similarity relations is discussed. The merged ontology is finally used to derive an integrated schema. The resulting schema can be used as the global schema in a federated database system.

Ontology reuse is turning into an important research issue in the ontology field. Ontology reuse can be seen from two points of view: (1) assembling, extending, specializing, adapting other ontologies which are parts of the resulting ontology, or (2) merging different ontologies on the same or similar subject into a single one that unifies all of them. The first kind of reuse is named integration and is the central issue of this paper. In this article, we characterize the integration process, describe and discuss the activities that compose this process and propose an integration methodology. This integration methodology has successfully been applied to build two ontologies in different domains by reusing publicly available ontologies.

The KBS Hyperbook System is a system which uses explicit conceptual models and meta data to structure and connect external data. When these external data are pages on the WWW, the corresponding conceptual model takes the role of an information index and determines the navigational structure between these pages (corresponding to one or more views on the external data). The conceptual model also serves as a schema for the integration of new pages (similar to the role of a database schema). In this paper we show how such a model can be used to support two main aspects of constructivist learning in a computer supported teaching environment, namely the integration of student projects into hyperbook based lecture material and the implementation and visualization of student annotations.

Guarino for their prompt responses to my questions. Third, to all my colleagues and friends in the Department of Spatial Information Science I would like to thank you for sharing the good and bad moments of my study life. I feel fortunate for having being part of a friendly environment that made my Ph.D. program an enjoyable and unforgettable experience. iii Fourth, I thank the support and funding from the University of Concepcin, Chile, and the initial funding from the Fulbright foundation. Further funding from the National Center of Geographic Information and Analysis, the National Imagery and Mapping Agency, and Lockheed Martin are gratefully acknowledged. Most important, I thank the continuous support, love, and patience of Christian and Alicia. This long journey would not have been possible without them. iv Table of Contents Acknowledgments .......................................................................................................ii List of Figu

Although ontology reuse is an important research issue only one of its subprocesses (merge) is fairly well understood. The time has come to change the current state of affairs with the other reuse subprocess: integration. In this paper we characterize the ontology integration process, we identify the activities that should be performed in this process and describe a methodology to perform the ontology integration process.

Information integration has been an important area of research for many years, and the problem of integration of geographic data has recently emerged. This paper presents an approach based on the use of Ontologies for solving the problem of semantic heterogeneity in the process of the construction of a Federated Schema in the framework of geographic data. We make use of standard technologies (abstract model and GML from OpenGIS, XMI based XML, SDTS from USGS). The principal ontology for the matching process is derived from Spatial Data Transfer Standard and WordNet. To obtain similarities and differences between entities from Export Schema, this work makes use of a semantic similarity model. The notion of context is also an important issue for the evaluation of semantic similarity. The main goal achieved in this work is the use of a Federated Architecture for Spatial Databases in conjunction with the assessment of semantic similarity across ontologies.

Data annotations provide an effective means to link data from diverse data archives to a domain conceptualization, e.g., ontology, which then provides users with an integrated and uniform view for querying the data. Existing approaches typically use document annotation techniques that require authors to embed linkage information into their documents. This prevents true collaborative extensions of documents by annotations and conceptual views of multiple users on the same data. This paper presents the concepts and architecture underlying a data annotation framework that enables external annotations of remote data at different levels of granularity. Semantic-carrying annotations, based on a domain conceptualization model, allow multiple users to enrich the same data under different perspectives. XML and related techniques are used for managing, exchanging, and querying annotations and conceptualizations.