Building of a Corporate Memory for Traffic-Accident Analysis

Article excerpt

This article presents an experiment of expertise capitalization in road traffic-accident analysis. We study the integration of models of expertise from different members of an organization into a coherent corporate expertise model. We present our elicitation protocol and the generic models and tools we exploited for knowledge modeling in this context of multiple experts. We compare the knowledge models obtained for seven experts in accidentology and their representation through conceptual graphs. Finally, we discuss the results of our experiment from a knowledge capitalization viewpoint.

There is an increasing industrial interest in the capitalization of knowledge (that is, both theoretical knowledge and practical know-how) of groups of people in an organization, such groups being possibly dispersed geographically. The coherent integration of this dispersed knowledge in a corporation is called corporate memory (Steels 1993). The memory of an enterprise includes not only a technical memory obtained by capitalization of its employees' know-how but also an organizational memory related to the past and present organizational structures of the enterprise (human resources, management, and so on) and project memories for capitalizing lessons and experience from given projects (Pomian 1996). Tourtier (1995) distinguishes profession memory, composed of the referential, documents, tools, and methods used in a given profession; society memory, related to organization, activities, products, and participants (for example, customers, suppliers, subcontractors); individual memory, characterized by status, competencies, know-how, and activities of a given member of the enterprise; and project memory, comprised of the project definition, activities, history, and results.

The construction of a corporate memory requires abilities to manage disparate knowhow and heterogeneous viewpoints, make this knowledge accessible to the adequate members of the enterprise, and integrate and store this knowledge in paper-based or electronic documents or in knowledge bases or case bases that should be easily accessible, usable, and maintainable. The solutions offered by research (Macintosh 1994) to this problem crucial in industry (Morizet-Mahoudeaux 1994) can be related to the analysis and modeling of an enterprise (Fox 1993; Fox, Chionglo, and Fadel 1993); its evolution through time; the experience acquired from past projects; the integration of models of expertise from different groups in an organization into a coherent corporate-expertise model; the construction and integration of distributed, heterogeneous knowledge bases or knowledge-based systems, possibly stemming from multiple experts; the development of an intelligent documentary system (Ballay and Poitou 1996; Poitou 1995); the management of hypertext links between knowledge bases and documents (Martin and Alpay 1996); knowledge sharing between different groups; the exploitation of Al techniques such as case-based reasoning (Simon 1996; Simon and Grandbastien 1995; Kitano et al. 1992); the exploitation of multiagent systems (Oliveira and Shmeil 1995; Vandenberghe and de Azevedo 1995); the exploitation of the web (Huynh, Popkin, and Stecker 1994); and natural language document-analysis techniques (Trigano 1994).

As noted in Nonaka (1991) and Van Engers et al. (1995), the knowledge chain consists of seven links: (1) listing the existing knowledge, (2) determining the required knowledge, (3) developing new knowledge, (4) allocating new and existing knowledge, (5) applying knowledge, (6) maintaining knowledge, and (7) disposing of knowledge. Thus, we can consider the building of the corporate memory as relying on the steps outlined in figure 1. Detection of needs in corporate memory: This needs detection can be based on advisability analysis, allowing the determination of crucial knowledge to be kept in the corporate memory (Grundstein and Barthes 1996), or on the exploitation of enterprise models (Uschold et al. …