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The objective of this research is to present a Multi-agent-based middleware that provides semantically-enabled information for SmES knowledge workers. The market currently offers several instruments for enabling knowledge management such as database management systems, data warehouse, intranet and extranet knowledge portals. However, these technological solutions do not take into account that knowledge management practices in small and medium companies are more congruous with apprenticeship-based learning rather than the formal training typical of big companies. This software is based on the European project E! 9770 PrEmISES and it helps small and medium enterprises to better exploit their information spaces. One central piece is the ontology component. In our days, ontologies are playing an important role. Many computer science domains, including software engineering, online learning, education and knowledge extraction are using ontologies in order to organize and share information in a semantic way. PrEmISES was the capability to couple with the existing data systems that are used by small and medium companies and in this way, to enhance them with a semantic layer/engine. The engine is used to find organizational documents within companies and make the searches more accurate by the use of ontologies. Premises is an improved software with educational purposes, based on the ubiquitous learning paradigm, that uses ontologies in order to find and organize relevant organizational documents, based on the employees work profile. In this way, employees can benefit of a fast-educational process (i.e. online learning) based on their individual work profiles. The engine is semantically enriched, meaning that it is searching for the specified words/query plus for semantically related concepts. In this paper we present PrEmISES architecture. We will present the main components and the main steps that were followed in order to develop the ontologies that were used for the ontology component. [ABSTRACT FROM AUTHOR]