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In this work we introduce a novel retrieval language, named OntoPath, for specifying and retrieving relevant ontology fragments. This language is intended to extract customized self-standing ontologies from very large, general-purpose ones. Through OntoPath, users can specify the desired detail level in the concept taxonomies as well as the properties between concepts that are required by the target applications. The syntax and aims of OntoPath resemble XPath's in that they are simple enough to be handled by non-expert users and they are designed to be included in other XML-based applications (e.g. transformations sheets, semantic annotation of web services, etc.). OntoPath has been implemented on the top of the graph-based database G, for which a Protégé OWL plug-in has been designed to access and retrieve ontology fragments. [ABSTRACT FROM AUTHOR]

The Health-e-Child project aims to develop an integrated healthcare platform for European paediatrics. In order to achieve a comprehensive view of children's health, a complex integration of biomedical data, information, and knowledge is necessary. Ontologies will be used to formally define this domain knowledge and will form the basis for the medical knowledge management system. This paper introduces an innovative methodology for the vertical integration of biomedical knowledge. This approach will be largely clinician-centered and will enable the definition of ontology fragments, connections between them (semantic bridges) and enriched ontology fragments (views). The strategy for the specification and capture of fragments, bridges and views is outlined with preliminary examples demonstrated in the collection of biomedical information from hospital databases, biomedical ontologies, and biomedical public databases. Keywords: Vertical Knowledge Integration, Approximate Queries, Ontology Views, Semantic Bridges. [ABSTRACT FROM AUTHOR]

In this paper we present and evaluate two approaches for the generation of Semantic Fields, which are used as a tool for resource discovery in the Semantic Web. We mainly concern ourselves with semantic networks that describe their interests and resources by means of ontologies. Semantic Fields are intended to help users to locate these resources by specifying a brief description (also as an ontology). We propose two ways to automatically build Semantic Fields. The first one is used in the KREIOS approach, which is based on the pre-computation of distances between all the ontology pairs. The second one is based on a fast incremental clustering algorithm, which groups together similar ontologies as they are published. These groups constitute a pre-computed set of Semantic Fields. Keywords: Resource discovery, similarity of ontologies, query distribution. [ABSTRACT FROM AUTHOR]