Know-linking: When Machine Learning Meets Organizational Tools Analysis to Generate Shared Knowledge in Large Companies - Archive ouverte HAL Access content directly
Book Sections Year : 2022

Know-linking: When Machine Learning Meets Organizational Tools Analysis to Generate Shared Knowledge in Large Companies

Abstract

When thousands of employees in a company meet a variety of resources and tools to build long term projects, the result is a knowledge explosion! the goal of companies since several years ago, is to build a successful strategy to manage knowledge. Traditional methods proved their limits in the era of data and connected devices, especially in enhancing links data among several services. In the current chapter we propose an approach based on profiling techniques as one support of knowledge sharing in large companies. Documents classification process allow us to link automatically profiles with documents that may potentially interest them. For this propose we present an algorithm of multi-labeled classification. On our behalf, building a list of profiles with indexed resources is not a finality, knowledge access and retrieval could be improved to more than 50% as our preliminary results proved, by exploring semantic links between profiles which are expressed deeply in documents.
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Dates and versions

hal-03842824 , version 1 (07-11-2022)

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Elamin Abderrahim, Nada Matta, Hassan Atifi. Know-linking: When Machine Learning Meets Organizational Tools Analysis to Generate Shared Knowledge in Large Companies. George A. Tsihrintzis. Handbook on Artificial Intelligence-Empowered Applied Software Engineering, 3, Springer, pp.71-88, 2022, Artificial Intelligence-Enhanced Software and Systems Engineering, 978-3-031-07650-3. ⟨10.1007/978-3-031-07650-3_5⟩. ⟨hal-03842824⟩
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