Skip to main content
SpringerLink
Log in

Using Ontology and Rough Set Building e-Commerce Knowledge Management System

  • Conference paper
Advances in Computer Science, Intelligent System and Environment

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 106))

Abstract

The variable precision rough-set model can be thought of as a generalization of the rough-set model. Rough set theory expresses vagueness not by means of membership, but by employing a boundary region of a set. The design of ontology can be based on classificatory knowledge or generic knowledge. The paper proposes using ontology and rough set to build the knowledge management model in e-commerce recommendation system, and to suffice the needs of theory and application in E-commerce recommendation system, and the experiments shows the CPU Time in the attribute numbers, indicating that ontology is superior to rough set in building the e-commerce knowledge management system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 5 (2001)

    Google Scholar 

  2. Beynon, M.: Reducts within the variable precision rough sets model: a further investigation. European Journal of Operational Research 134, 592–605 (2001)

    Article  MATH  Google Scholar 

  3. Maedche, A., Staab, S.: Ontology Learning for the Semantic Web. Special Issue on the Semantic Web, IEEE Intelligent System 16(2), 72–79 (2001)

    Google Scholar 

  4. Navigli, R., Velardip, Gangemi, A.: Ontology learning and its application to automated terminology translation. IEEE Intelligent Systems 18(l), 22–31 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Network and Information Center, Qingyuan Polytechnic, Qingyuan, 511510, China

    Guo Li & QiHua Peng

Authors
  1. Guo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. QiHua Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Wuhan Section Special No.1, International Science & Education Researcher Association, Jiangxia Road of Wuhan, Wuhan, China

    David Jin

  2. Guangzhou Section, International Science & Education Researcher Association, No.144, Jinheng Road Jinbi Garden 85-1102 southern Road of Industry Avenue Haizhu District, Guangzhou, Guang Dong provience, China

    Sally Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, G., Peng, Q. (2011). Using Ontology and Rough Set Building e-Commerce Knowledge Management System. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23753-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23753-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23752-2

  • Online ISBN: 978-3-642-23753-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation