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Qualitative indexation and risk management in the pharmaceutical industry : application to the transgenic animal


In 2007, Michael Spence, 2001 Nobel-prize-winning economist, when asked “is there a leading solution in risk management”, he answered “there is an opportunity”. Information Retrieval Methodology is becoming an indispensable tool to manage risk and provide support to an efficient decision making. This PhD work validates this thesis on an example of Research and Development of transgenic animal technologies. In particular we demonstrate the strength of this methodology in context of development of an early warning system focused on detection of 8 risks and lobbying actions designed to influence legislation and public opinion. A conventional bibliographic research based on keyword search tends to give a large corpus with number of irrelevant documents and documents often ranked inadequately in terms of importance. The end-user is thus forced to browse the suggested documents manually in search for relevant information. Intuitively he or she may identify non-obvious relations between the collected information and the subject based on his/her understanding of the field. However such documents would be discarded or low-ranked in a conventional search. Our search methodology automatises this natural interpretation and understanding process. In practice this challenge is addressed by adding extra metadata to structured databases including internet web sites. This metadata provide indexation of search results by means of an in-depth knowledge based on the facts and relations (i.e. understanding) between them as provided by the end user. Furthermore our approach avoids the need to master multiple indexation languages and overrides the Google search ranking system. Hence this methodology combines information retrieval with features of competitive intelligence build through close cooperation between the information scientists and end-users

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