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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/17024

Title: Managing corporate memory on the semantic web
Authors: Khilwani, Nitesh
Harding, Jennifer A.
Keywords: Corporate memory management
Latent semantic analysis
RDF (resource description framework)
Semantic web
Text mining
Issue Date: 2016
Publisher: © Springer Science+Business Media
Citation: KHILWANI, N. and HARDING, J.A., 2016. Managing corporate memory on the semantic web. Journal of Intelligent Manufacturing, 27(1), pp.101-118.
Abstract: Corporate memory (CM) is the total body of data, information and knowledge required to deliver the strategic aims and objectives of an organization. In the current market, the rapidly increasing volume of unstructured documents in the enterprises has brought the challenge of building an autonomic framework to acquire, represent, learn and maintain CM, and efficiently reason from it to aid in knowledge discovery and reuse. The concept of semantic web is being introduced in the enterprises to structure information in a machine readable way and enhance the understandability of the disparate information. Due to the continual popularity of the semantic web, this paper develops a framework for CM management on the semantic web. The proposed approach gleans information from the documents, converts into a semantic web resource using resource description framework (RDF) and RDF Schema and then identifies relations among them using latent semantic analysis technique. The efficacy of the proposed approach is demonstrated through empirical experiments conducted on two case studies. © 2014 Springer Science+Business Media New York.
Description: This article was accepted for publication in the Journal of Intelligent Manufacturing [© Springer Science+Business Media]. The final publication is available at Springer via http://dx.doi.org/10.1007/s10845-013-0865-4
Version: Accepted for publication
DOI: 10.1007/s10845-013-0865-4
URI: https://dspace.lboro.ac.uk/2134/17024
Publisher Link: http://dx.doi.org/10.1007/s10845-013-0865-4
ISSN: 0956-5515
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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