Back
 IJIS  Vol.5 No.5 , October 2015
DBpedia-Based Fuzzy Query Recommenda-tion Algorithm and Its Applications in the Resource-Sharing Platform of Polar Samples
Abstract: In order to continuously promote the polar sample resource services in China and effectively guide the users to access such information as needed, a fuzzy algorithm based on DBpedia has been proposed through the analysis of the characteristics of the query recommendations in search engines, namely, to search similar entry queues by constructing a DBpedia category tree, then use the fuzzy matching algorithm to work out the entry similarity, and then present the example query applications of this algorithm on the resource-sharing platform of polar samples. Comparing the traditional literal character matching method and DBpedia semantic similarity algorithm, the experimental results show that the fuzzy query algorithm based on DBpedia features has a higher search accuracy rate, stronger anti-interference capability, and more flexible algorithm use by virtue of its fuzzy weight adjustment.
Cite this paper: Cheng, W. , Wu, Q. , Cheng, X. , Zhang, J. , Song, Z. (2015) DBpedia-Based Fuzzy Query Recommenda-tion Algorithm and Its Applications in the Resource-Sharing Platform of Polar Samples. International Journal of Intelligence Science, 5, 196-207. doi: 10.4236/ijis.2015.55017.
References

[1]   Anagnostopoulos, A., Becchetti, L., Castillo, C., et al. (2010) An Optimization Framework for Query Recommendation. Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, 161-170.
http://dx.doi.org/10.1145/1718487.1718508

[2]   Cheng, W.F., Zhang, J., Xia, M.Y., et al. (2013) System Design and Implementation of a Resource-Sharing Platform for Polar Samples. Polar Research, 25, 185-196.

[3]   Linden, G., Smith, B. and York, J. (2003) Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 7, 76-80.
http://dx.doi.org/10.1109/MIC.2003.1167344

[4]   Bai, R.J., Yu, X.F. and Wang, X.Y. (2011) The Comparative Analysis of Major Domestic and Foreign Ontology Library. New Technology of Library and Information Service, 1, 3-13.

[5]   Gabrilovich, E. and Markovitch, S. (2007) Computing Semantic Relatedness Using Wikipedia-Based Explicit Semantic Analysis. IJCAI, 7, 1606-1611.

[6]   Sheng, Z.-C. and Tao, X.-P. (2011) Semantic Similarity Computing Method Based on Wikipedia. Computer Engineering, 37.

[7]   Liu, J. and Yao, T.-F. (2010) Semantic Relevancy Computing Based on Wikipedia. Computer Engineering, 36.

[8]   Chao, L.M., Zhang, Y. and Xing, C.X. (2011) DBpedia and Its Typical Applications. New Technology of Library and Information Service, 3, 80-87.

[9]   Breiman, L., Friedman, J.H., Oishen, R.A., et al. (1984) Classification and Regression Trees. Wadsworth, Inc.

[10]   Chen, Z.W., Wu, Q.E and Yang, W.D. (2015) Target Image Classification through Encryption Algorithm Based on the Biological Features. International Journal of Intelligence Science (IJIS), 5, 6-12.
http://dx.doi.org/10.4236/ijis.2015.51002

[11]   Bezdek, J.C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.
http://dx.doi.org/10.1007/978-1-4757-0450-1

[12]   Qing, L., Xu, X.-D. and Wang, S.-T. (2012) Fuzzy Particle Filter for Object Tracking. AASRI Procedia, 3, 191-196.
http://dx.doi.org/10.1016/j.aasri.2012.11.032

[13]   Meia, W., Xiao, Y. and Wang, G. (2012) Object Classification Based on a Combination of Possibility and Probability Likelihood in the Bayesian Framework. Procedia Engineering, 29, 9-14.
http://dx.doi.org/10.1016/j.proeng.2011.12.659

[14]   Mai, F.-J., Li, D.-P. and Yue, X.-G. (2011) Research on Chinese Word Segmentation Based on Bi-Direction Marching Method and Feature Selection Algorithm. Journal of Kunming University of Science and Technology (Natural Science Edition), 36, 47-51.

[15]   Martelli, A., Ravenscroft, A. and Ascher, D. (2008) Python Cookbook. O’Reilly.

[16]   Wiki API.
http://zh.wikipedia.org/w/api.php

 
 
Top