Automatic knowledge extraction from manufacturing research publications

P. Boonyasopon 1 Andreas Riel 1 Wilhelm Uys 2 Louis Louw 2 S. Tichkiewitch 1 N. Du Preez
1 G-SCOP_CPP - CPP
G-SCOP - Laboratoire des sciences pour la conception, l'optimisation et la production
Abstract : Knowledge mining is a young and rapidly growing discipline aiming at automatically identifying valuable knowledge in digital documents. This paper presents the results of a study of the application of document retrieval and text mining techniques to extract knowledge from CIRP research papers. The target is to find out if and how such tools can help researchers to find relevant publications in a cluster of papers and increase the citation indices their own papers. Two different approaches to automatic topic identification are investigated. One is based on Latent Dirichlet Allocation of a huge document set, the other uses Wikipedia to discover significant words in papers. The study uses a combination of both approaches to propose a new approach to efficient and intelligent knowledge mining.
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CIRP Annals - Manufacturing Technology, Elsevier, 2011, 60 (1), pp.477-480. 〈10.1016/j.cirp.2011.03.043〉
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https://hal.archives-ouvertes.fr/hal-01964585
Contributeur : Andreas Riel <>
Soumis le : mercredi 9 janvier 2019 - 11:47:59
Dernière modification le : vendredi 11 janvier 2019 - 09:55:49

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P. Boonyasopon, Andreas Riel, Wilhelm Uys, Louis Louw, S. Tichkiewitch, et al.. Automatic knowledge extraction from manufacturing research publications. CIRP Annals - Manufacturing Technology, Elsevier, 2011, 60 (1), pp.477-480. 〈10.1016/j.cirp.2011.03.043〉. 〈hal-01964585〉

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