An Agent Based Catalog Integration System through Active Learning

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-28 Number-4
Year of Publication : 2015
Authors : G.Sindhu Priya, P.Krubhala, P.Niranjana
DOI :  10.14445/22312803/IJCTT-V28P132


G.Sindhu Priya, P.Krubhala, P.Niranjana "An Agent Based Catalog Integration System through Active Learning". International Journal of Computer Trends and Technology (IJCTT) V28(4):172-175, October 2015. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Online Commercial data integration plays a vital role in categorizing the products from multiple providers all over the globe. An unique taxonomy is maintained by the Commercial portals and products of the providers are associated with their own taxonomy. In the existing work, an efficient and scalable approach to Catalog Integration is used which is based on the use of Source Category and Taxonomy structure Information. We formulate this intuition as a structured prediction optimization problem. Learning algorithms can actively query the user for labels. Active learning concept is used to identify candidate products for labeling and also used to obtain the desired outputs at new data points. It intends to develop the catalog integration process in automated fashion in an agent based environment in which agent can cooperate interact with the consumers to find the best classification based upon the consumer preferences.

[1] R. Agrawal and R. Srikant, “On Integrating Catalogs,” Proc. 10th Int’lConf. World Wide Web (WWW), pp. 603-612, 2001.
[2] Nandi and P.A. Bernstein, “Hamster: Using Search Click logs for Schema and Taxonomy Matching,” Proc. VLDB Endowment, vol. 2,no. 1, pp. 181-192, 2009.
[3] D. Zhang, X. Wang, and Y. Dong, “Web Taxonomy Integration Using Spectral Graph Transducer,” Proc. ER Workshop, pp. 300- 312, 2004.
[4] D. Zhang and W.S. Lee, “Web Taxonomy Integration through Co-Bootstrapping,” Proc. 27th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 410- 417, 2004.
[5] D. Zhang and W.S. Lee, “Web Taxonomy Integration Using Support Vector Machines,” Proc. 13th Int’l Conf. World Wide Web (WWW),pp. 472-481, 2004.
[6] S. Sarawagi, S. Chakrabarti, and S. Godbole, “Cross- Training:Learning Probabilistic Mappings between Topics,” Proc. Ninth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data mining (KDD),2003.
[7] J. Kleinberg and E. Tardos, “Approximation Algorithms for Classification Problems with Pairwise Relationships: Metric Labeling and Markov Random Fields,” J. ACM, vol. 49, no. 5, pp. 616-639, 2002.
[8] P. Ravikumar and J. Lafferty, “Quadratic Programming Relaxations for Metric Labeling and Markov Random Field Map Estimation,” Proc.23rd Int’l Conf. Machine Learning (ICML), pp. 737 744, 2006.
[9] H. Daume´ III, J. Langford, and D. Marcu, “Search-Based Structured Prediction,” Machine Learning J., vol. 75, pp. 297- 325, 2009.
[10] E. Rahm and P. Bernstein, “A Survey of Approaches to Automatic Schema Matching,” The VLDB J., vol. 10, no. 4, pp. 334-350, 2001.

Active learning, Catalog Integration, classification, Master taxonomy, Provider taxonomy, Agent.