Generating shared knowledge between several Actors in a large company

Autor: Nada Matta, Hassan Atifi, Elamin Abderrahim, Vincent Maugis
Přispěvatelé: Laboratoire Informatique et Société Numérique (LIST3N), Université de Technologie de Troyes (UTT), Agence Nationale pour la Gestion des Déchets Radioactifs (ANDRA), TECHnologies pour la Coopération, l’Interaction et les COnnaissances dans les collectifs (Tech-CICO), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS
IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, Oct 2020, Toronto, Canada. pp.1292-1297, ⟨10.1109/SMC42975.2020.9283373⟩
SMC
DOI: 10.1109/SMC42975.2020.9283373⟩
Popis: In a big company, at least 1000 employees from different fields are collaborating together to build complex projects, in the background, they are producing a daily important volume of data and knowledge resources in different domains. Today, Knowledge represents the key of development of enterprises as Lewis Platt the president and CEO of Hewlett-Packard says "If HP knew what HP knows, we would be three times more productive". Existing tools like documents management, still not sufficient, especially to share knowledge between different fields. Due to the complexity of their tasks, actors in an organization need information from each other, but they don’t have any idea about data production and documents contents of each other. Proposed solutions like the use of Index with keywords proves its limits. In this setting, we propose to gather all the actors having the same characteristics in enterprise, into the same structure using a hybrid-profiling method. After linking extracted profiles with their resources of knowledge using an index-based profiling algorithm, we generate a conceptual graph for each profile to identify its capital of knowledge resources and its collaboration with other profiles. On the other hand, We highlight a special link between profiles collaborating indirectly together discovered by semantic analysis of document contents, which strongly influences knowledge access and documents retrieval.
Databáze: OpenAIRE