Loughborough University
Semantic Corporate Memory_revised_resubmitted_NK_final.pdf (1.01 MB)

Managing corporate memory on the semantic web

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journal contribution
posted on 2015-03-17, 13:55 authored by Nitesh Khilwani, Jennifer HardingJennifer Harding
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.



  • Mechanical, Electrical and Manufacturing Engineering

Published in

Journal of Intelligent Manufacturing


1 - 18


KHILWANI, N. and HARDING, J.A., 2016. Managing corporate memory on the semantic web. Journal of Intelligent Manufacturing, 27(1), pp.101-118.


© Springer Science+Business Media


  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date



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






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