The huge number of autonomous and heterogeneous data repositories accessible on the "global information infrastructure" makes it impossible for users to be aware of the locations, structure/organization, query languages and semantics of the data in various repositories. There is a critical need to complement current browsing, navigational and information retrieval techniques with a strategy that focuses on information content and semantics. In any strategy that focuses on information content, the most critical problem is that of different vocabularies used to describe similar information across domains. We discuss a scalable approach for vocabulary sharing. The objects in the repositories are represented as intensional descriptions by pre-existing ontologies expressed in Description Logics characterizing information in different domains. User queries are rewritten by using interontology relationships to obtain semanticspreserving translations across the ontologies.

I n a m ultidatabase system, schematic con icts between two objects are usually of interest only when the objects have some semantic similarity. In this paper we try to reconcile the schematic and semantic perspectives. We propose the concept of semantic proximity, w h i c h is essentially an abstraction/mapping between the domains of the two objects associated with the context of comparison. The need for making explicit the meaning and use of an object provides the motivation for explicit representation of the semantic fulcrum, i.e., the context of comparison. A partial representation of context as a collection of contextual coordinates and their values is proposed. The semantics of the speci city relationship between two c o n texts is de ned. The contexts are organized as a meet semi-lattice 1 . Associated operations like the greatest lower bound (glb) o f t wo c o n texts and other operations are also de ned.These operations along with the information on the type of abstractions used to relate two object classes form the basis of a semantic taxonomy. T h e schematic and data con icts between object classes are enumerated and classi ed. We then try to achieve the reconciliation of the semantic and schematic perspectives by discussing possible semantic similarities between two object classes that have v arious types of schematic and data con icts.We i n troduce a uniform formalism called schema correspondences to represent structural similarities between the object classes. At t h e semantic level the intensional description of the object classes in a database is provided by the context expressed in a description logic like language. The schema correspondences use a modi ed object algebra to store mappings from the semantic level to the actual data organization in the databases and are associated with the respective c o n texts. We again try to achieve the reconciliation of the semantic and schematic perspectives by modeling the schema correspondences as the projection of semantic proximity with respect to (wrt) c o n text. Changes in the context lead to changes in the schema correspondences. An algebra to model these is also presented and explained with the help of illustrative examples.

Background: A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature.

The experience in using the framework and the preliminary evaluation indicate that this approach has promise in creating structured knowledge, to implement in CDS systems, that is usable across organizations.