Abstract. The Semantic Web is a powerful vision that is getting to grips with the challenge of providing more human-oriented web services. Hence, reasoning with and across distributed, partially implicit assumptions (contextual knowledge), is a milestone. Ontologies are a primary means to deploy the Semantic Web vision, but few work has been done on them to manage the context-dependency of Web knowledge. In this paper we introduce an ontology for representing a variety of reified contexts and states of affairs, called D&S, currently implemented as a plug-in to the DOLCE foundational ontology, and its application to two cases: an ontology for communication situations and roles, and an ontology for peer-to-peer communication. The reified contexts represented in D&S have a rich structure, and are a middleware between full-fledged formal contexts and theories, and the often poor vocabularies implemented in Web ontologies... 1

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Abstract: The development of new ontologies does not tap the full potential of existing knowledge sources and ongoing ontology engineering methodologies do not address ontology reuse to a satisfactory extent yet. In this paper we analyze the challenges related to the reuse process on the basis of two scenarios in the domains of eRecruitment and medicine, which aim at building domain ontologies by reusing existing knowledge sources.

Abstract. The Semantic Network, a component of the Unified Medical Language System ® (UMLS), describes core biomedical knowledge consisting of semantic types and relationships. It is a well established, semi-formal ontology in widespread use for over a decade. We expected to “publish ” this ontology on the Semantic Web, using OWL, with relatively little effort. However, we ran into a number of problems concerning alternative interpretations of the SN notation and the inability to express some of the interpretations in OWL. We detail these problems, as a cautionary tale to others planning to publish preexisting ontologies on the Semantic Web, as a list of issues to consider when describing formally concepts in any ontology, and as a collection of criteria for evaluating alternative representations, which could form part of a methodology of ontology development.

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Taxonomies are commonly used for organizing knowledge, particularly in biomedicine where the taxonomy of living organisms and the classification of diseases are central to the domain. The principles used to produce taxonomies are either intrinsic (properties of the partial ordering relation) or added to make knowledge more manageable (opposition of siblings and economy). The applicability of these principles in the biomedical domain is presented using the Unified Medical Language System (UMLS) and issues raised by the application of these principles are illustrated. While intrinsic principles are not challenged, we argue that the opposition of siblings brings to bear excessive constraints on a domain ontology and that the adverse effects of economy may outweigh its benefits. The two-level structure used in the UMLS is discussed.

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In this paper we propose an analysis and an upgrade of WordNet's top-level synset taxonomy of nouns. We briefly review WordNet and identify its main semantic limitations. Some principles from a forthcoming OntoClean methodology are applied to the ontological analysis of WordNet. A revised top-level taxonomy is proposed, which is meant to be conceptually more rigorous, cognitively transparent, and efficiently exploitable in several applications. This work is a revision and extension of [3].

Physical, Independent, Relative, and Mediating [17]. Such diversity in the top level of ULOs illustrates the potential difficulties in generalizing our results.

Existing efforts on ontology mapping, alignment and merging vary from methodological and theoretical frameworks, to methods and tools that support the semi-automatic coordination of ontologies. However, only latest research efforts "touch" on the mapping /merging of ontologies using the whole breadth of available knowledge. This paper aims to thoroughly describe the HCONE approach on ontology merging. The approach described is based on (a) capturing the intended informal interpretations of concepts by mapping them to WordNet senses using lexical semantic indexing, and (b) exploiting the formal semantics of concepts' definitions by means of description logics' reasoning services.