The Community for Technology Leaders
RSS Icon
Issue No.06 - June (2009 vol.21)
pp: 785-799
Hai Zhuge , Institute of Computing Technology, Chinese Academy of Sciences, Beijing
The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.
Community discovery, e-learning, emerging semantics, semantic community, Semantic Link Network.
Hai Zhuge, "Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning", IEEE Transactions on Knowledge & Data Engineering, vol.21, no. 6, pp. 785-799, June 2009, doi:10.1109/TKDE.2008.141
[1] R. Albert and A.L. Barabasi, “Statistical Mechanics of Complex Networks,” Rev. Modem Physics, vol. 74, no. 1, pp. 47-97, 2002.
[2] Y.Y. Ahn et al., “Semantic Web and Web 2.0: Analysis of Topological Characteristics of Huge Online Social Networking Services,” Proc. 16th Int'l Conf. World Wide Web (WWW '07), May 2007.
[3] B. Aleman-Meza et al. “Social Networks: Semantic Analytics on Social Networks: Experiences in Addressing the Problem of Conflict of Interest Detection,” Proc. 15th Int'l Conf. World Wide Web (WWW), 2006.
[4] R.M. Assuncao et al., “Efficient Regionalization Techniques for Socio-Economic Geographical Units Using Minimum Spanning Trees,” Int'l J. Geographical Information Science, vol. 7, no. 20, pp.797-811, 2006.
[5] L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan, “Group Formation in Large Social Networks: Membership, Growth, and Evolution,” Proc. ACM SIGKDD '06, Aug. 2006.
[6] T. Berners-Lee, J. Hendler, and O. Lassila, “Semantic Web,” Scientific Am., vol. 284, no. 5, pp. 34-43, 2001.
[7] U. Brandes, “A Faster Algorithm for Betweenness Centrality,” J.Math. Sociology, vol. 25, no. 2, pp. 163-177, 2001.
[8] P. Brusilovsky, “KnowledgeTree: A Distributed Architecture for Adaptive e-Learning,” Proc. 13th Int'l Conf. World Wide Web (WWW '04), pp. 104-113, 2004.
[9] D. Cai et al., “Mining Hidden Community in Heterogeneous Social Networks,” Proc. Third Int'l Workshop Link Discovery (LinkKDD '05), Aug. 2005.
[10] R. Farrell, S.D. Liburd, and J.C. Thomas, “Dynamic Assembly of Learning Objects,” Proc. 13th Int'l Conf. World Wide Web (WWW '04), pp. 162-169, May 2004.
[11] L. Freeman, “A Set of Measures of Centrality Based upon Betweeness,” Sociometry, vol. 40, pp. 35-41, 1977.
[12] M. Girvan and M. Newman, “Community Structure in Social and Biological Networks,” Proc. Nat'l Academy of Sciences of the USA, pp. 8271-8276, 2002.
[13] J. Golbeck and J. Hendler, “Inferring Binary Trust Relationships in Web-Based Social Networks,” ACM Trans. Internet Technology, vol. 6, no. 4, Nov. 2006.
[14] R. Guimera et al., Self-Similar Community Structure in Organizations, arXiv:cond-mat/0211498, (22), 2002.
[15] L. Hossain, A. Wu, and K.K.S. Chung, “Social Networks and Coordination Patterns: Actor Centrality Correlates to Project Based Coordination,” Proc. 20th Anniversary Conf. Computer Supported Cooperative Work (CSCW '06), Nov. 2006.
[16] H. Kautz, B. Selman, and M. Shah, “Referral Web: Combining Social Networks and Collaborative Filtering,” Comm. ACM, vol. 3, no. 40, pp. 63-65, 1997.
[17] B.W. Kernighan and S. Lin, “An Efficient Heuristic Procedure for Partitioning Graphs,” Bell System Technical J., vol. 49, pp. 291-307, 1970.
[18] R. Kumar, J. Novak, and A. Tomkins, “Structure and Evolution of Online Social Networks,” Proc. ACM SIGKDD '06, Aug. 2006.
[19] Q. Li, R.W.H. Lau, T.K. Shih, and F.W.B. Li, “Technology Supports for Distributed and Collaborative Learning over the Internet,” ACM Trans. Internet Technology, vol. 8, no. 2, 2008.
[20] M. Makrehchi and M.S. Kamel, “Learning Social Networks from Web Documents Using Support Vector Classifiers,” Proc. IEEE/WIC/ACM Int'l Conf. Web Intelligence (WI '06), Dec. 2006.
[21] Y. Matsuo et al., “POLYPHONET: An Advanced Social Network Extraction System from the Web,” Proc. 15th Int'l Conf. World Wide Web (WWW), 2006.
[22] M.E.J. Newman and M. Girvan, “Finding and Evaluating Community Structure in Networks,” Physical Rev. E, vol. 69, no. 2,, 2004. doi: 10.1103/PhysRevE.69.026113.
[23] M.E.J. Newman, “Fast Algorithm for Detecting Community Structure in Networks,” arXiv:cond-mat/0309508, (22), 2003.
[24] M.E.J. Newman, “Who Is the Best Connected Scientist? A Study of Scientific Coauthorship Networks,” Physical Rev. E, vol. 64, 2001.
[25] M.E.J. Newman, “Scientific Collaboration Networks. II. Shortest Paths, Weighted Networks, and Centrality,” Physical Rev. E, vol. 64, 016132.
[26] F. Radicchi et al., “Defining and Identifying Communities in Networks,” Proc. Nat'l Academy of Sciences of the USA, vol. 101, pp.2658-2663, 2004.
[27] A. Renyi, “On Measures of Entropy and Information,” Proc. Fourth Berkeley Symp. Math. Statistics and Probability, pp. 547-561, 1961.
[28] J.R. Tyler, D.M. Wilkinson, and B.A. Huberman, “Email as Spectroscopy: Automated Discovery of Community Structure within Organizations,” Proc. First Int'l Conf. Communities and Technologies, pp. 81-96, 2003.
[29] L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web,” Technical Report SIDL-WP-1999-0120, Stanford Digital Libraries, 1999.
[30] A.P. Pons, “Object Prefetching Using Semantic Links,” ACM SIGMIS Database, vol. 37, no. 1, pp. 97-109, 2006.
[31] J.M. Pujol, R. Sangüesa, and J. Delgado, “Extracting Reputation in Multi Agent Systems by Means of Social Network Topology,” Proc. First Int'l Joint Conf. Autonomous Agents and Multiagent Systems (AAMAS '02), July 2002.
[32] M.J. Rattigan, M. Maier, and D. Jensen, “Graph Clustering with Network Structure Indices,” Proc. 24th Ann. Int'l Conf. Machine Learning (ICML '07), / proceedings/ icml2007/papers407.pdf, 2007.
[33] C.E. Shannon and W. Weiner, The Mathematical Theory of Communication. Univ. of Illinois Press, 1963.
[34] J.P. Vert, “Adaptive Context Trees and Text Clustering,” IEEE Trans. Information Theory, vol. 47, no. 5, pp. 1884-1901, 2001.
[35] D.M. Wilkinson and B.A. Huberman, “A Method for Finding Communities of Related Genes,” Proc. Nat'l Academy of Sciences of the USA, vol. 101, pp. 5241-5248, 2004.
[36] F. Wu and B.A. Huberman, “Finding Communities in Linear Time: A Physics Approach,” European Physical J. B, vol. 38, pp.331-338, 2004.
[37] B. Yang, W.K. Cheung, and J. Liu, “Community Mining from Signed Social Networks,” IEEE Trans. Knowledge and Data Eng., vol. 19, no. 10, pp. 1333-1348, Oct. 2007.
[38] B. Yang and J. Liu, “Discovering Global Network Communities Based on Local Centralities,” ACM Trans. Web, vol. 2, no. 1, Feb. 2008.
[39] W.W. Zachary, “An Information Flow Model for Conflict and Fission in Small Groups,” J. Anthropological Research, vol. 33, pp.452-473, 1977.
[40] H. Zhuge, The Knowledge Grid. World Scientific, 2004.
[41] H. Zhuge, R. Jia, and J. Liu, “Semantic Link Network Builder and Intelligent Semantic Browser,” Concurrency and Computation: Practice and Experience, vol. 16, no. 14, pp. 1453-1476, 2004.
[42] H. Zhuge, “Autonomous Semantic Link Networking Model for the Knowledge Grid,” Concurrency and Computation: Practice and Experience, vol. 7, no. 19, pp. 1065-1085, 2007.
[43] H. Zhuge and X. Li, “Peer-to-Peer in Metric Space and Semantic Space,” IEEE Trans. Knowledge and Data Eng., vol. 19, no. 6, pp.759-771, June 2007.
29 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool