A number of computer science problems, including heterogeneous database integration, natural language processing, document intelligent retrieval would benefit from the capability to model the absolute meaning of things, independently of any particular use of these things. Such models, termed ontologies, have been heavily investigated over the last ten years, with various purposes and within various contexts. The goal of this paper is to investigate the role of ontologies for data integration and to present an ontology model that was precisely developed to allow neutral exchange and automatic integration of technical data. We first propose a taxonomy of ontologies into linguistic ontologies, based on words and usable for intelligent document processing, and concept ontologies, multilingual and usable with structured data. We then discuss differences between ontologies and usual conceptual models. We claim that the main difference is context-sensitivity, and we identify four requirements for making ontologies less contextual than models and suitable for data integration. Finally we present how these requirements have been fulfilled in the PLIB ontology model developed to give meaning to technical data, and we outline the use of PLIB-based ontologies in various domains including database integration, e-enginecring and the semantic web.
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