Wuhan University Journal of Natural Sciences

, Volume 11, Issue 1, pp 165–169 | Cite as

Incorporating domain knowledge into data mining process: An ontology based framework

  • Pan Ding
  • Shen Jun-yi
  • Zhou Mu-xin
Web Application Framework and Architecture


With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. First, some valid mining task schedules are generated, and then autonomous and local mining are executed periodically, finally, previous results are merged and refined. The framework based on the model creates a communication mechanism to incorporate domain knowledge into continuous process through ontology service. The local and merge mining are transparent to the end user and heterogeneous data source by ontology. Experiments suggest that the framework should be useful in guiding the continous mining process.

Key words

continuous data mining domain knowledge ontology framework 

CLC number

TP 311 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Kopanas I, Avouris N M, Daskalaki S. The Role of Domain Knowledge in a Large Scale Data Mining Project.Proc. of the SETN 2002, LNAI 2308, New York: Springer, 2002, 288–299.Google Scholar
  2. [2]
    Agrawal R, Psaila G. Active Data Mining.Proc of the KDD'95. California: AAAI Press, 1995. 3–8.Google Scholar
  3. [3]
    Cheung D, Han Jia-wei, Ng V,et al.. Maintenance of Discovered Association Rules in Large Databases.Proc of the ICDE'96. Los Alamitos: IEEE CS, 1996. 106–114.Google Scholar
  4. [4]
    Cheng Hong, Yan Xi-feng, Han Jia-wei. IncSpan: Incremental Mining of Sequential Patterns in Large Database.Proc of ACM SIGKDD'04. New York: ACM Press, 2004. 527–532.Google Scholar
  5. [5]
    Spilipoulou M, Roddick J. Higher Order Mining.Proc of 2nd Data Mining Methods and Databases. Southampton: WIT Press, 2000. 309–320.Google Scholar
  6. [6]
    Gupta S K, Bhatnagar V, Wasan S K,et al. Intension Mining. TR No.IITD/CSE/TR2000/001. Delhi: Indian Institute of Technology, 2000.Google Scholar
  7. [7]
    Yoon S C, Henschen L J, Park E K,et al. Using Domain Knowledge in Knowledge Discovery.Proc of the CIKM'99. New York: ACM Press, 1999. 243–250.Google Scholar
  8. [8]
    Guarino N. Formal Ontology and Information Systems.Proc of the FOIS'98. Amsterdam: IOS Press, 1998. 3–15.Google Scholar
  9. [9]
    FIPA. FIPA Ontology Service Specification (XC00086D).http://www.fipa.org/specs/fipa00086/XC000086D. pdf. 2001.Google Scholar
  10. [10]
    Bernstein A, Provost F, Hill S. Toward Intelligent Assistance for a Data Mining Process.IEEE Transactions on Knowledge and Data Engineering, 2005,17(4):503–518.CrossRefGoogle Scholar
  11. [11]
    Fong J, Wong H K, Huang S M. Continuous and Incremental Data Mining Association Rules Using Frame Metadata Model.Knowledge Based System, 2003,16(2):91–100.CrossRefGoogle Scholar
  12. [12]
    Pan Ding, Shen Jun-yi. Research on Software Architecture for Real-Time Metadata Management.Journal of Xi'an Jiaotong University, 2005,39(6) (Ch).Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Pan Ding
    • 1
  • Shen Jun-yi
    • 1
  • Zhou Mu-xin
    • 2
  1. 1.Department of Computer Science and TechnologyXi'an Jiaotong UniversityXi'an ShaanxiChina
  2. 2.Department of Computer Science and EngineeringShanghai Jiaotong UniversityShanghaiChina

Personalised recommendations