Automatic knowledge extraction from manufacturing research publications
Date
2011
Authors
Boonyasopon P.
Riel A.
Uys W.
Louw L.
Tichkiewitch S.
Du Preez N.
Journal Title
Journal ISSN
Volume Title
Publisher
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. © 2011 CIRP.
Description
Keywords
Decision making, Document retrieval technique, Management, Citation index, Digital Documents, Document Retrieval, Document retrieval technique, Knowledge extraction, Knowledge mining, Latent Dirichlet allocation, Manufacturing research, Research papers, Text mining techniques, Topic identification, Wikipedia, Decision making, Industrial research, Data mining
Citation
CIRP Annals - Manufacturing Technology
60
1
http://www.scopus.com/inward/record.url?eid=2-s2.0-79957661722&partnerID=40&md5=b74bb5ac0fb99e56851cad3503de5799
60
1
http://www.scopus.com/inward/record.url?eid=2-s2.0-79957661722&partnerID=40&md5=b74bb5ac0fb99e56851cad3503de5799