Total views : 518

A Knowledge Representation Technique for Intelligent Storage and Efficient Retrieval using Knowledge based Markup Language


  • Department of Information Technology, K.L.N. College of Information Technology, Sivagangai, Madurai – 630612, Tamil Nadu, India
  • Department of Computer Science and Engineering, K.L.N. College of Information Technology, Sivagangai, Madurai – 630612, Tamil Nadu


Background: Knowledge Engineering is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise. Knowledge Engineering is the technique applied by knowledge engineers to build intelligent systems: Expert Systems, Knowledge Based Systems, Knowledge based Decision Support Systems, Expert Database Systems etc. Methods: This work builds a Knowledge Base using Knowledge Base Markup Language (KBML) which is derived from XML architecture. All the Meta information is stored in a KBML file whereas the actual data may be available in any data source. Findings: This system also provides facilities to search/add the contents to and from the Knowledge Base dynamically. The experimental results show that the system provides a high precision, recall and f-measure values which proves the high relevance of the retrieved values. Applications: Builds an intelligent system for Edaphology domain which concerns with plants and its related soil features. It provides the edaphologists and agriculturists by retrieving relevant and useful information about the plants. This is of huge importance as plant growth and yield are directly dependent on the soil features.


Data Source, Edaphology, Knowledge Base Markup Language, Meta Information

Full Text:

 |  (PDF views: 290)


  • Qwaider W Q. Integrated of knowledge management and E- learning system. International Journal of Hybrid Information Technology. 2011; 4(4):55–70.
  • Ramya ST, Rangarajan P. Knowledge based methods for video data retrieval. International Journal of Computer Science & Information Technology. 2011; 3(5):167–75.
  • Ralph LL, Ellis TJ. An investigation of a knowledge management solution for the improvement of reference services. Journal of Information Technology and Organizations. 2009; 4:17–38.
  • Moayer S, Gardner S. Integration of data mining within a strategic knowledge management framework. International Journal of Advanced Computer Science and Applications. 2012; 3(8):67–72.
  • Ben-Zvi T. The efficacy of business simulation games in creating decision support systems. International Journal of Decision Support System. 2010; 49(1):61–69.
  • Eldin SS, Ahmed ASE, Elsayed A. Using of conceptual representation approach for query expansion in information retrieval. International Journal of Engineering & Computer Science. 2012; 12(4):55–65.
  • Anyanwu MN, Shiva SG. Comparative analysis of serial decision tree classification algorithms. International Journal of Computer Science and Security. 2009; 3(3):230–40.
  • Kebede G. Knowledge management: An information science perspective. International Journal of Information Management. 2010; 30(5):416–24.
  • Mollahosseini A, Barkhordar M. Supplier knowledge management for supplier development. International Journal of Business Information System. 2010;14(4):17–26.
  • Kohail SN, El-Halees AM. Implementation of data mining techniques for meteorological data analysis. International Journal of Information and Communication Technology Research. 2011; 1(3):96–102.
  • Khattak AM, Khan AM, Lee S, Lee YK. Analyzing association rule mining and clustering on sales day knowledge with XL miner and weka. International Journal of Knowledgebase Theory and Application. 2010; 3(1):13–22.
  • Kumar V, Rathee N. Knowledge discovery from database using an integration of clustering and classification. International Journal of Advanced Computer Science and Applications. 2011; 2(3):29–33.
  • Kulkarni MSV. Mining knowledge using decision tree algorithm. International Journal of Scientific & Engineering Research. 2011; 2(5):1–6.
  • Kavitha V, Punithavalli M. Clustering time series data stream – a literature survey. International Journal of Computer Science and Information Security. 2010; 8(1):29–37.
  • Nie G, Rowec W, Zhanga L, Tiana Y, Shi Y. Credit cards churn forecasting by logistic regression and decision tree. International Journal of Expert Systems with Applications. 2011; 38(1):15273–85.
  • Vohra R, Das NN. Intelligent decision support systems for admission management in higher education institutes. International Journal of Artificial Intelligence & Applications. 2011; 2(4):63–70.
  • Patil RA, Ahire P G, Patil PD, Golande AL. Decision tree post processing for extraction of actionable knowledge. International Journal of Engineering and Innovative Technology. 2012; 2(1):152–55.
  • Tanwar P, Prasad TV, Datta K. Comparative study of three declarative knowledge representation techniques. International Journal on Computer Science and Engineering. 2010; 2(7):2274–81.
  • Rao VS. Multi agent-based distributed knowledge mining: an over view. International Journal of Reviews in Computing. 2010; 3:83–92.
  • Silwattananusarn T, Tuamsuk K. Data mining and its applications for knowledge management. International Journal of Data Mining & Knowledge Management Process. 2012; 2(5):13–24.
  • Venkatesan E, Velmurugan T. Performance analysis of decision tree algorithms for breast cancer classification. Indian Journal of Science and Technology. 2005;8(29):1–8. doi: 10.17485/ijst/2015/v8i1/84646.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.