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On Knowledge Organization and Management for Innovation: Modeling with the Strategic Observation Approach in Material Science

General introduction

This contribution joins an information system (IS) construction work aimed at promotion and innovation in nanosciences [SID 14a, SID 14b] and particularly in material sciences in a large French laboratory. It involves an interdisciplinary approach to natural language processing (NLP) and knowledge organization (KO) and its management (KM) with an aim to develop an approach of technological cooperation between two highly technological areas: research laboratories and the industrial world.

The Jean Lamour Institute (IJL) in Nancy (France), one of the largest mixed research units, is made up of several centers of expertise dedicated to material analysis. One of these centers, the Technology and Knowledge Transfer Office, aims to bring fundamental research and the socio-economic world together through a technological cooperation program. Being able to identify and structure the Institute’s expertise, the laboratory’s skills and know-how in order to offer exhaustive skills in its regional, national or international environment therefore lie at the heart of the work of this center of expertise.

Chapter written by Sahbi Sidhom and Philippe Lambert.

Collective Intelligence and Digital Archives: Towards Knowledge Ecosystems, First Edition. Edited by Samuel Szoniecky and Nasreddine Bouhai'.

© ISTE Ltd 2017. Published by ISTE Ltd and John Wiley & Sons, Inc.

The main aim of our research is to have a method that allows the semiautomatic extraction (man-machine) of scientific and technological information to establish a cartography of the IJL’s expertise with the final goal of developing the innovation approach between the IJL and manufacturers.

The laboratory has a heterogeneous, corpus, taking an inventory of the research activities of different teams and the activities of centers of expertise. There are more than 250 researchers and educators - researchers who are split into four departments on several sites (Nancy, Metz and Epinal), with significant tacit knowledge possessed by researchers that is difficult for scientific communities (regional or national) to identify and exploit.

The working hypothesis that underlies our proposition is that the creation of a tree view of classes bringing together and belonging to a study domain ontology [BUI 05] would allow our project of offering expertise in material science to be structured and to explicitly bring out the Institute’s best domains of expertise.

The national evaluation policy that French research laboratories must follow produces a series of documents (reports, analyses, questionnaires, promotions, AERES/www.aeres-evaluation.fr for evaluation reports, etc.). This volume of documents remains relatively significant and complex in its processing, but it allows contributions to be made to the analysis of our issue. The application of our approach in the trilogy (skills to be promoted, corpus of activities, ontology for promotion and innovation in material science) aims to give governmental (or state) instances or decision makers (university presidents) a “cliche” of laboratory activities (in terms of skills, know-how, technical knowledge, etc.). In time (occasionally) and for a given period (4-5 years), the instances may respond to international requests for proposals or recommendations in technological “challenges” for innovation.

Study of the corpus reproduced the “Cognitive Redaction Model” [SID 02]. This revelation in language structures actually hides a stability of writing in the evaluation texts that we use to implement a hierarchy of classes (see Figure 7.1). A rigorous methodology in KM depending on the use of NLP technologies allows specific information (or knowledge entity) extraction processes to be set in motion with an aim to define the laboratory’s operational fields.

Therefore, the chapter outline that remains in tune with the adopted methodology is subdivided into three large processes. The first step consists in adapting the KM approach (or MASK) to our logical sequence of harnessing different heritage elements of the IJL to establish a book (or dictionary) of the organization’s knowledge. This becomes an indispensable reference work for the innovation approach. The second step concerns TAL technologies, particularly the ad hoc creation of resources for exploiting our corpus and value-added information extraction processes (see Figure 7.1). The third step lies in the formalization and structuration of information in a hierarchy class of knowledge to structure skill offers in our IS.

Identification of a cognitive writing grammar [SID 02]

Figure 7.1. Identification of a cognitive writing grammar [SID 02]

In the research outline provided, it will be a matter of demonstrating that the adopted methodology will allow satisfying results to be provided and a robust indexing structure to be had for identifiable skills; also that the class hierarchy of knowledge (or the innovative material domain ontology) will allow the scientific evolution of the laboratory to be followed and the laboratory to be repositioned in a larger group gathering scientific communities in the domain; and lastly, that a developed conceptual cartography will serve the technological observation processes [LAM 11] in order to respond to recommendation subjects in a strategic development process.

 
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