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.
Document type :
Journal articles
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01964585
Contributor : Andreas Riel <>
Submitted on : Wednesday, January 9, 2019 - 11:47:59 AM
Last modification on : Monday, April 8, 2019 - 10:26:22 AM

File

CIRP Annals 2011 Boonyasopon e...
Files produced by the author(s)

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

28

Files downloads

54