EconPapers    
Economics at your fingertips  
 

Managing corporate memory on the semantic web

Nitesh Khilwani and J. A. Harding ()
Additional contact information
Nitesh Khilwani: Loughborough University
J. A. Harding: Loughborough University

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 1, No 7, 118 pages

Abstract: 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.

Keywords: Corporate memory management; Semantic web; Ontology; Text mining; RDF (resource description framework); Latent semantic analysis (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-013-0865-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:27:y:2016:i:1:d:10.1007_s10845-013-0865-4

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-013-0865-4

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:joinma:v:27:y:2016:i:1:d:10.1007_s10845-013-0865-4