IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v27y2016i1d10.1007_s10845-013-0865-4.html
   My bibliography  Save this article

Managing corporate memory on the semantic web

Author

Listed:
  • Nitesh Khilwani

    (Loughborough University)

  • J. A. Harding

    (Loughborough University)

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.

Suggested Citation

  • Nitesh Khilwani & J. A. Harding, 2016. "Managing corporate memory on the semantic web," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 101-118, February.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:1:d:10.1007_s10845-013-0865-4
    DOI: 10.1007/s10845-013-0865-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-013-0865-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-013-0865-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Christian Bizer & Tom Heath & Tim Berners-Lee, 2009. "Linked Data - The Story So Far," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 5(3), pages 1-22, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhaoguang Xu & Yanzhong Dang & Peter Munro & Yuhang Wang, 2020. "A data-driven approach for constructing the component-failure mode matrix for FMEA," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 249-265, January.
    2. Yuval Cohen & Gonen Singer, 2021. "A smart process controller framework for Industry 4.0 settings," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1975-1995, October.
    3. Peter Chhim & Ratna Babu Chinnam & Noureddin Sadawi, 2019. "Product design and manufacturing process based ontology for manufacturing knowledge reuse," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 905-916, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stahl, Florian & Schomm, Fabian & Vossen, Gottfried, 2012. "Marketplaces for data: An initial survey," ERCIS Working Papers 14, University of Münster, European Research Center for Information Systems (ERCIS).
    2. Anett HOPPE & Ana ROXIN & Christophe NICOLLE, 2015. "Ontology-based Integration of Web Navigation for Dynamic User Profiling," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 19(1), pages 10-24.
    3. Anne E Thessen & Cynthia Sims Parr, 2014. "Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    4. Kurt Sandkuhl & Hans-Georg Fill & Stijn Hoppenbrouwers & John Krogstie & Florian Matthes & Andreas Opdahl & Gerhard Schwabe & Ömer Uludag & Robert Winter, 2018. "From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(1), pages 69-80, February.
    5. Phillip Lord & Simon Cockell & Robert Stevens, 2012. "Three Steps to Heaven: Semantic Publishing in a Real World Workflow," Future Internet, MDPI, vol. 4(4), pages 1-12, November.
    6. Marta Sabou & Irem Onder & Adrian M. P. Brasoveanu & Arno Scharl, 2016. "Towards cross-domain data analytics in tourism: a linked data based approach," Information Technology & Tourism, Springer, vol. 16(1), pages 71-101, March.
    7. Wuhui Chen & Incheon Paik, 2013. "Improving efficiency of service discovery using Linked data-based service publication," Information Systems Frontiers, Springer, vol. 15(4), pages 613-625, September.
    8. Tianxing Wu & Guilin Qi & Cheng Li & Meng Wang, 2018. "A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications," Sustainability, MDPI, vol. 10(9), pages 1-26, September.
    9. Veale, Michael & Binns, Reuben, 2017. "Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data," SocArXiv ustxg, Center for Open Science.
    10. Schiavone, Francesco & Paolone, Francesco & Mancini, Daniela, 2019. "Business model innovation for urban smartization," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 210-219.
    11. Ghadeer Ashour & Ahmed Al-Dubai & Imed Romdhani & Daniyal Alghazzawi, 2022. "Ontology-Based Linked Data to Support Decision-Making within Universities," Mathematics, MDPI, vol. 10(17), pages 1-21, September.
    12. E. G. Stephan & T. O. Elsethagen & L. K. Berg & M. C. Macduff & P. R. Paulson & W. J. Shaw & C. Sivaraman & W. P. Smith & A. Wynne, 2016. "Semantic catalog of things, services, and data to support a wind data management facility," Information Systems Frontiers, Springer, vol. 18(4), pages 679-691, August.
    13. Hossein Hassani & Xu Huang & Mansi Ghodsi, 2018. "Big Data and Causality," Annals of Data Science, Springer, vol. 5(2), pages 133-156, June.
    14. Muhammad Sajid Qureshi & Ali Daud, 2021. "Fine-grained academic rankings: mapping affiliation of the influential researchers with the top ranked HEIs," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8331-8361, October.
    15. Sean Kennedy & Owen Molloy & Robert Stewart & Paul Jacob & Maria Maleshkova & Frank Doheny, 2012. "A Semantically Automated Protocol Adapter for Mapping SOAP Web Services to RESTful HTTP Format to Enable the Web Infrastructure, Enhance Web Service Interoperability and Ease Web Service Migration," Future Internet, MDPI, vol. 4(2), pages 1-24, April.
    16. Simon French, 2012. "Expert Judgment, Meta-analysis, and Participatory Risk Analysis," Decision Analysis, INFORMS, vol. 9(2), pages 119-127, June.
    17. Costantino Thanos, 2017. "Research Data Reusability: Conceptual Foundations, Barriers and Enabling Technologies," Publications, MDPI, vol. 5(1), pages 1-19, January.
    18. Raymond Y. K. Lau & J. Leon Zhao & Wenping Zhang & Yi Cai & Eric W. T. Ngai, 2015. "Learning Context-Sensitive Domain Ontologies from Folksonomies: A Cognitively Motivated Method," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 561-578, August.
    19. Muhammad Ahtisham Aslam & Naif Radi Aljohani, 2017. "SPedia: A Central Hub for the Linked Open Data of Scientific Publications," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 128-147, January.
    20. Costantino Thanos, 2016. "A Vision for Open Cyber-Scholarly Infrastructures," Publications, MDPI, vol. 4(2), pages 1-18, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:27:y:2016:i:1:d:10.1007_s10845-013-0865-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.