Using a Semantic Self-Organising Web Page-Ranking Mechanism for Public Administration and Education
In the proposed method for Web page-ranking, a
novel theoretic model is introduced and tested by examples of order
relationships among IP addresses. Ranking is induced using a
convexity feature, which is learned according to these examples
using a self-organizing procedure. We consider the problem of selforganizing
learning from IP data to be represented by a semi-random
convex polygon procedure, in which the vertices correspond to IP
addresses. Based on recent developments in our regularization
theory for convex polygons and corresponding Euclidean distance
based methods for classification, we develop an algorithmic
framework for learning ranking functions based on a Computational
Geometric Theory. We show that our algorithm is generic, and
present experimental results explaining the potential of our approach.
In addition, we explain the generality of our approach by showing its
possible use as a visualization tool for data obtained from diverse
domains, such as Public Administration and Education.
[1] A. Sidiropoulos, et al., "Prefetching in Content Distribution Networks,"
Communities Identification and Outsourcing World Wide Web, vol. 11,
pp. 39-70, 2008.
[2] D. Katsaros, Y. Manolopoulos, "Caching in Web memory hierarchies,"
In: Proceedings of the ACM Symposium on Applied Computing (SAC),
Nicosia, 14-17 March, 2004.
[3] S. Sivasubramanian, G. Pierre, M van Steen, G. Alonso, "Analysis of
caching and replication strategies for web applications," IEEE Internet
Computing, vol. 11, no. 1, pp. 60-66, 2007.
[4] A. Vakali, "Proxy cache replacement algorithms: a history-based
approach," World Wide Web J, vol. 4, no. 4, pp. 277-298, 2001.
[5] T. Kroeger, E. D. Long, J. Mogul, "Exploring the bounds of Web latency
reduction from caching and perfecting", In: Proceedings of the USENIX
Symposium on Internet Technologies and Services (USITS), Monterey,
8-11 December, 1997.
[6] D. Katsaros, Y. Manolopoulos, "Prediction in wireless networks by
Markov chains," IEEE Wireless Communications magazine, 1977.
[7] A. Nanopoulos, D. Katsaros, Y. Manolopoulos, "A data mining
algorithm for generalized Web prefetching," IEEE Transactions on
Knowedgeand Data Engineering, vol. 15, no. 5, pp. 1155-1169, 2003.
[8] L. Page, S. Brin, R. Motwani and T. Winograd, The pagerank citation
ranking: Bringing order to the web. Stanford Digital Library
Technologies Project, Tech. Rep. Paper SIDL-WP-1999-0120 (version
of 11/11/1999).
[9] H. Yang, I. King, "Predictive Random Graph Ranking on the Web,"
2006 International Joint Conference on Neural Networks, Vancouver
Canada, July 16-21, 2006.
[10] A. Shivani, "Ranking on Graph Data," In Proceedings of the 23rd
International Conference on Machine Learning, Pittsburgh, PA, 2006.
[11] W. W. Cohen, E. R. Schapire, & Y. Singer, "Learning to order things,"
Journal of Artificial Intelligence Research, vol. 10, pp. 243-270, 1999.
[12] R. Herbrich, T. Graepel, & K. Obermayer, Large margin rank
boundaries for ordinal regression, Advances in Large Margin
Classifiers. Liu Press, 1997, pp. 115-132.
[13] K. Crammer, Y. Singer, "Ranking with ranking," In Proceedings of the
Advances in Neural Information Processing Systems, 2002.
[14] Y. Freund, R. Iyer, E. R Schapire, Y. Singer, "An efficient boosting
algorithm for combining preferences," Journal of Machine Learning
Research, vol. 4, pp. 933-969, 2003.
[15] M. Poulos, S. Papavlasopoulos and V. Chrissicopoulos, "A Text
Categorization Technique based on a Numerical Conversion of a
Symbolic Expression and an Onion Layers Algorithm," Journal of
Digital Information (JoDI), v. 6. 1, 2004.
[16] M. Poulos et al., "Specific Selection of FFT Amplitudes from Audio
Sports and News Broadcasting for Classification Purposes," Journal of
Graph Algorithms and Applications, vol. 11, no. 1, pp. 277-307, 2007.
[17] M. Poulos, G. Bokos, F. Vaioulis, "Towards the semantic extraction of
digital signatures for librarian image-identification purposes," Journal of
the American Society for Information Science and Technology, vol. 59,
no. 5, pp. 708 - 718, 2008.
[18] L. R. Graham, An efficient algorithm for determining the convex hull of
a finite planar set. Inform, Proc. Letters, pp. 132-133, 1972.
[1] A. Sidiropoulos, et al., "Prefetching in Content Distribution Networks,"
Communities Identification and Outsourcing World Wide Web, vol. 11,
pp. 39-70, 2008.
[2] D. Katsaros, Y. Manolopoulos, "Caching in Web memory hierarchies,"
In: Proceedings of the ACM Symposium on Applied Computing (SAC),
Nicosia, 14-17 March, 2004.
[3] S. Sivasubramanian, G. Pierre, M van Steen, G. Alonso, "Analysis of
caching and replication strategies for web applications," IEEE Internet
Computing, vol. 11, no. 1, pp. 60-66, 2007.
[4] A. Vakali, "Proxy cache replacement algorithms: a history-based
approach," World Wide Web J, vol. 4, no. 4, pp. 277-298, 2001.
[5] T. Kroeger, E. D. Long, J. Mogul, "Exploring the bounds of Web latency
reduction from caching and perfecting", In: Proceedings of the USENIX
Symposium on Internet Technologies and Services (USITS), Monterey,
8-11 December, 1997.
[6] D. Katsaros, Y. Manolopoulos, "Prediction in wireless networks by
Markov chains," IEEE Wireless Communications magazine, 1977.
[7] A. Nanopoulos, D. Katsaros, Y. Manolopoulos, "A data mining
algorithm for generalized Web prefetching," IEEE Transactions on
Knowedgeand Data Engineering, vol. 15, no. 5, pp. 1155-1169, 2003.
[8] L. Page, S. Brin, R. Motwani and T. Winograd, The pagerank citation
ranking: Bringing order to the web. Stanford Digital Library
Technologies Project, Tech. Rep. Paper SIDL-WP-1999-0120 (version
of 11/11/1999).
[9] H. Yang, I. King, "Predictive Random Graph Ranking on the Web,"
2006 International Joint Conference on Neural Networks, Vancouver
Canada, July 16-21, 2006.
[10] A. Shivani, "Ranking on Graph Data," In Proceedings of the 23rd
International Conference on Machine Learning, Pittsburgh, PA, 2006.
[11] W. W. Cohen, E. R. Schapire, & Y. Singer, "Learning to order things,"
Journal of Artificial Intelligence Research, vol. 10, pp. 243-270, 1999.
[12] R. Herbrich, T. Graepel, & K. Obermayer, Large margin rank
boundaries for ordinal regression, Advances in Large Margin
Classifiers. Liu Press, 1997, pp. 115-132.
[13] K. Crammer, Y. Singer, "Ranking with ranking," In Proceedings of the
Advances in Neural Information Processing Systems, 2002.
[14] Y. Freund, R. Iyer, E. R Schapire, Y. Singer, "An efficient boosting
algorithm for combining preferences," Journal of Machine Learning
Research, vol. 4, pp. 933-969, 2003.
[15] M. Poulos, S. Papavlasopoulos and V. Chrissicopoulos, "A Text
Categorization Technique based on a Numerical Conversion of a
Symbolic Expression and an Onion Layers Algorithm," Journal of
Digital Information (JoDI), v. 6. 1, 2004.
[16] M. Poulos et al., "Specific Selection of FFT Amplitudes from Audio
Sports and News Broadcasting for Classification Purposes," Journal of
Graph Algorithms and Applications, vol. 11, no. 1, pp. 277-307, 2007.
[17] M. Poulos, G. Bokos, F. Vaioulis, "Towards the semantic extraction of
digital signatures for librarian image-identification purposes," Journal of
the American Society for Information Science and Technology, vol. 59,
no. 5, pp. 708 - 718, 2008.
[18] L. R. Graham, An efficient algorithm for determining the convex hull of
a finite planar set. Inform, Proc. Letters, pp. 132-133, 1972.
@article{"International Journal of Business, Human and Social Sciences:57751", author = "Marios Poulos and Sozon Papavlasopoulos and V. S. Belesiotis", title = "Using a Semantic Self-Organising Web Page-Ranking Mechanism for Public Administration and Education", abstract = "In the proposed method for Web page-ranking, a
novel theoretic model is introduced and tested by examples of order
relationships among IP addresses. Ranking is induced using a
convexity feature, which is learned according to these examples
using a self-organizing procedure. We consider the problem of selforganizing
learning from IP data to be represented by a semi-random
convex polygon procedure, in which the vertices correspond to IP
addresses. Based on recent developments in our regularization
theory for convex polygons and corresponding Euclidean distance
based methods for classification, we develop an algorithmic
framework for learning ranking functions based on a Computational
Geometric Theory. We show that our algorithm is generic, and
present experimental results explaining the potential of our approach.
In addition, we explain the generality of our approach by showing its
possible use as a visualization tool for data obtained from diverse
domains, such as Public Administration and Education.", keywords = "Computational Geometry, Education, e-Governance,Semantic Web.", volume = "4", number = "3", pages = "222-5", }
