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.

In recent years, as a knowledge-based discipline, bioinformatics has moved to make its knowledge more computationally amenable. After its beginnings in the disciplines as a technology advocated by computer scientists to overcome problems of heterogeneity, ontology has been taken up by the biologists themselves as a means to consistently annotate features from genotype to phenotype. In medical informatics, artifacts called ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. In this article, we review the current position in ontologies and how they have become institutionalized within biomedicine. As the field has matured, the much older philosophical aspects of ontology have come into play. With this and the institutionalization of ontology has come greater formality. We review this trend and what benefits it might bring to ontologies and their use within biomedicine. Author biographies:

This paper reports the results of a series of experiments designed to establish how non-expert subjects conceptualize geospatial phenomena. Subjects were asked to give examples of geographical categories in response to a series of differently phrased elicitations. The results yield an ontology of geographical categories---a catalogue of the prime geospatial concepts and categories shared in common by human subjects independently of their exposure to scientific geography. When combined with nouns such as feature and object, the adjective geographic elicited almost exclusively elements of the physical environment of geographical scale or size, such as mountain, lake, and river. The phrase things that could be portrayed on a map, on the other hand, produced many geographical scale artefacts (roads, cities, etc.) and flat objects (states, countries, etc.), as well as some physical feature types. These data reveal considerable mismatch as between the meanings assigned to the terms `geography' and `geographic' by scientific geographers and by ordinary subjects, so that scientific geographers are not in fact studying geographical phenomena as such phenomena are conceptualized by nave subjects. The data suggest, rather, a special role in determining the subject-matter of scientific geography for the concept of what can be portrayed on a map. This work has implications for work on usability and interoperability in geographical information science, and it throws light also on subtle and hitherto unexplored ways in which ontological terms such as `object', `entity', and `feature' interact with geographical concepts.

Motivation: Molecular biology databases hold a large number of empirical facts about many different aspects of biological entities. That data is static in the sense that one cannot ask a database "What effect has protein A on gene B?" or "Do gene A and gene B interact, and if so, how?". Those questions require an explicit model of the target organism. Traditionally, biochemical systems are modelled using kinetics and differential equations in a quantitative simulator. For many biological processes, however, detailed quantitative information is not available, only qualitative or fuzzy statements about the nature of interactions. Results: We designed and implemented a qualitative simulation model of phage growth control in E. coli based on the existing simulation environment QSim. Qualitative reasoning can serve as the basis for automatic transformation of contents of genomic databases into interactive modelling systems that can reason about the relations and interactions of biological...

The paper begins with the question "Do mountains exist?" It shows that providing an answer to this question is surprisingly difficult, and that the answer which one gives depends on the context in which the question is posed. Mountains clearly exist as real correlates of everyday human thought and action, and they form the archetype for geographic objects. Yet individual mountains lack many of the properties that characterize bona fide objects, and mountains as a category also lack many of the properties that characterize natural kinds. In the context of scientific modeling of the environment, especially of such phenomena as surface hydrology and fluvial erosion and deposition, mountains are not picked out as constituents of reality in their own right at all; rather they are just parts of the field of elevations whose gradients direct the direction of runoff and influence the intensity of erosion. While an object-based ontology of mountains and other landforms is thus required to do justice to our everyday conceptions of the environment, topographic databases designed to support environmental modeling can be field-based at geographic scales. Where objects are needed to model surface processes these tend to be much smaller, typically individual pebbles or sediment grains, plus occasionally individual organisms or built structures.

The success of the Semantic Web crucially depends on the easy creation, integration, and use of semantic data. For this purpose, we consider an integration scenario that defies core assumptions of current metadata construction methods. We describe a framework of metadata creation where Web pages are generated from a database and the database owner is cooperatively participating in the Semantic Web. This leads us to the deep annotation of the database---directly by annotation of the logical database schema or indirectly by annotation of the Web presentation generated from the database contents. From this annotation, one may execute data mapping and/or migration steps, and thus prepare the data for use in the Semantic Web. We consider deep annotation as particularly valid because: (i) dynamic Web pages generated from databases outnumber static Web pages, (ii) deep annotation may be a very intuitive way to create semantic data from a database, and (iii) data from databases should remain where it can be handled most efficiently---in its databases. Interested users can then query this data directly or choose to materialize the data as RDF files.

Scientific discovery in data rich domains (e.g., biological sciences, atmospheric sciences) presents several challenges in information extraction and knowledge acquisition from heterogeneous, distributed, autonomously operated, dynamic data sources. This paper describes these problems and outlines the key elements of algorithmic and systems solutions for computer assisted scientific discovery in such domains. These include: ontology-assisted approaches to customizable data integration and information extraction from heterogeneous, distributed data sources; distributed data mining algorithms for knowledge acquisition from large, distributed data sets which obviate the need for transmitting large volumes of data across the network; ontology-driven approaches to exploratory data analysis from alternative ontological perspectives; and modular and extensible agent-based implementations of the algorithms within a platform-independent agent infrastructure. Prototype implementations of ...

Numerous genome projects worldwide produce and gather gigabytes of sequence, structure, cellular, metabolic and other types of information to be stored in a large number of autonomous databases. In this context, supercomputers are used for sequence and structure comparison of objects within one database. The task of searching for common motifs and biologically related pieces of information in large-scale, heterogeneous databases is computationally even more expensive. Current approaches require the preparation of a single data pool prior to analysis. The immediate use of several heterogeneous and autonomous databases in molecular biology is prohibited by a great heterogeneity on many levels, especially on the semantic level of defining the meaning of database categories. Here, molecular biology has a communication problem. For example, even fundamental technical terms as "gene" and "protein sequence" are used inconsistently by researchers and major international genomic and protein dat...

We present an architecture of collaborating software agents which is supposed to tackle the problem of information integration in a flexible and generic way. An intended system is supposed to offer a transparent view onto information resources (data sources and software tools) for genomic research and to be able to adopt to the constantly evolving environment. This framework architecture is currently used for building a prototype of IGD-GIS (Integrated Genomic Database- Genome Information System).

CONTENTS: 1 OBJECTIVE........................................................................................................................2 2 GENERAL SCIENTIFIC STATUS.....................................................................................2 3 FORMAL ONTOLOGY IN INFORMATION SYSTEMS...................................................2 4 CURRENT STATUS OF TOP-LEVEL ONTOLOGY.........................................................3 5 APPLICATIONS IN THE FIELD OF CLINICAL TRIALS................................................3 5.1 CLINICAL TRIALS: AIMS AND RELEVANCE.......................................................................3 5.2 STATUS OF PREVIOUS AND PRESENT MEDICAL CLASSIFICATION SYSTEMS..........................4 5.3 TOWARDS A FUTURE GENERATION OF COMPUTER-BASED CLINICAL<