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The overall objective of this research is to engineer a decision aid approach to support the identification of collaborative networks which could then constitute potential Virtual Breeding Environments. An interesting aspect of this approach is working with internet and thus considered an "open universe" of potential partners. In this perspective, the paper puts the focus on extracting essential facets of firm competences using an ontology approach. The method followed to construct the ontology is presented as well as a brief introduction on its use. This work is part of an on going project to produce cognitive aids to support decisions when seeking to form partnerships and establish VBEs

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International audienceThis paper focuses on a subtask of the QUAERO1 research program, a major innovating research project related to the automatic processing of multimedia and multilingual content. The objective discussed in this article is to propose a new method for the classification of scientific papers, developed in the context of an international patents classification plan related to the same field. The practical purpose of this work is to provide an assistance tool to experts in their task of evaluation of the originality and novelty of a patent, by offering to the latter the most relevant scientific citations. This issue raises new challenges in categorization research as the patent classification plan is not directly adapted to the structure of scientific documents, classes have high citation or cited topic and that there is not always a balanced distribution of the available examples within the different learning classes. We propose, as a solution to this problem, to apply an improved K-nearest-neighbors (KNN) algorithm based on the exploitation of association rules occurring between the index terms of the documents and the ones of the patent classes. By using a reference dataset of patents belonging to the field of pharmacology, on the one hand, and a bibliographic dataset of the same field issued from the Medline collection, on the other hand, we show that this new approach, which combines the advantages of numerical and symbolical approaches, improves considerably categorization performance, as compared to the usual categorization methods