Semantic peer, here are the neighbors you want!

ABSTRACT

Peer Data Management Systems (PDMSs) have been introduced as a solution to the problem of large-scale sharing of semantically rich data. A PDMS consists of semantic peers connected through semantic mappings. Querying a PDMS may lead to very poor results, because of the semantic degradation due to the approximations given by the traversal of the semantic mappings, thus leading to the problem of how to boost a network of mappings in a PDMS.

In this paper we propose a strategy for the incremental maintenance of a flexible network organization that clusters together peers which are semantically related in Semantic Overlay Networks (SONs), while maintaining a high degree of node autonomy. Semantic features, a summarized representation of clusters, are stored in a "light" structure which effectively assists a newly entering peer when choosing its semantically closest overlay networks. Then, each peer is supported in the selection of its own neighbors within each overlay network according to two policies: Range-based selection and k-NN selection. For both policies, we introduce specific algorithms which exploit a distributed indexing mechanism for efficient network navigation. The proposed approach has been implemented in a prototype where its effectiveness and efficiency have been extensively tested.

REFERENCES

• 1: K. Aberer, P. Cudré-Mauroux, M. Hauswirth, and T. V. Pelt. GridVine: Building Internet-Scale Semantic Overlay Networks. In Proc. of ISWC, pages 107--121, 2004.
• 2: Mayank Bawa , Gurmeet Singh Manku , Prabhakar Raghavan, SETS: search enhanced by topic segmentation, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860491]
• 3: T. Berners-Lee, J. Hendler, and O. Lassila. The Semantic Web. Scientific American, May 2001.
• 4: Paolo Ciaccia , Marco Patella , Pavel Zezula, M-tree: An Efficient Access Method for Similarity Search in Metric Spaces, Proceedings of the 23rd International Conference on Very Large Data Bases, p.426-435, August 25-29, 1997
• 5: C. Comito, S. Patarin, and D. Talia. PARIS: A Peer-to-Peer Architecture for Large-Scale Semantic Data Integration. In Proc. of the DBISP2P Workshop, pages 163--170, 2005.
• 6: A. Crespo and H. Garcia-Molina. Semantic Overlay Networks for P2P Systems. In Proc. of the 3rd AP2PC Workshop, pages 1--13, 2004.
• 7: C. Doulkeridis, K. Nørvåg, and M. Vazirgiannis. DESENT: Decentralized and Distributed Semantic Overlay Generation in P2P Networks. IEEE J. on Selected Areas in Comm., 25(1):25--34, 2007.
• 8: Prasanna Ganesan , Hector Garcia-Molina , Jennifer Widom, Exploiting hierarchical domain structure to compute similarity, ACM Transactions on Information Systems (TOIS), v.21 n.1, p.64-93, January 2003 [doi>10.1145/635484.635487]
• 9: Venkatesh Ganti , Raghu Ramakrishnan , Johannes Gehrke , Allison Powell, Clustering Large Datasets in Arbitrary Metric Spaces, Proceedings of the 15th International Conference on Data Engineering, p.502, March 23-26, 1999
• 10: The Piazza Peer Data Management System, IEEE Transactions on Knowledge and Data Engineering, v.16 n.7, p.787-798, July 2004 [doi>10.1109/TKDE.2004.1318562]
• 11: A. K. Jain , M. N. Murty , P. J. Flynn, Data clustering: a review, ACM Computing Surveys (CSUR), v.31 n.3, p.264-323, Sept. 1999 [doi>10.1145/331499.331504]
• 12: G. Koloniari and E. Pitoura. Content-Based Routing of Path Queries in Peer-to-Peer Systems. In Proc. of the 9th EDBT Conf., pages 29--47, 2004.
• 13: C. Leacock and M. Chodorow. Combining Local Context and WordNet Similarity for Word Sense Identification. In C. Fellbaum, editor, WordNet: An Electronic Lexical Database, pages 256--283. MIT Press, 1998.
• 14: Mei Li , Wang-Chien Lee , Anand Sivasubramaniam, Semantic Small World: An Overlay Network for Peer-to-Peer Search, Proceedings of the 12th IEEE International Conference on Network Protocols, p.228-238, October 05-08, 2004
• 15: Alessandro Linari , Gerhard Weikum, Efficient peer-to-peer semantic overlay networks based on statistical language models, Proceedings of the international workshop on Information retrieval in peer-to-peer networks, November 11-11, 2006, Arlington, Virginia, USA [doi>10.1145/1183579.1183582]
• 16: J. Madhavan, S. Cohen, X. Dong, A. Halevy, S. Jeffery, D. Ko, and C. Yu. Web-Scale Data Integration: You Can Afford to Pay as You Go. In CIDR, pages 342--350, 2007.
• 17: Federica Mandreoli , Riccardo Martoglia , Simona Sassatelli , Wilma Penzo, SRI: exploiting semantic information for effective query routing in a PDMS, Proceedings of the 8th annual ACM international workshop on Web information and data management, November 10-10, 2006, Arlington, Virginia, USA [doi>10.1145/1183550.1183556]
• 18: F. Mandreoli, R. Martoglia, W. Penzo, S. Sassatelli, and G. Villani. SRI@work: Efficient and Effective Routing Strategies in a PDMS. In In Proc. of the 8th WISE Conf., pages 285--297, 2007.
• 19: F. Mandreoli, R. Martoglia, W. Penzo, S. Sassatelli, and G. Villani. SUNRISE: Exploring PDMS Networks with Semantic Routing Indexes. In Proc. of ESWC, 2007.
• 20: Wolfgang Nejdl , Martin Wolpers , Wolf Siberski , Christoph Schmitz , Mario Schlosser , Ingo Brunkhorst , Alexander Löser, Super-peer-based routing and clustering strategies for RDF-based peer-to-peer networks, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775229]
• 21: Josiane Xavier Parreira , Sebastian Michel , Gerhard Weikum, p2pDating: Real life inspired semantic overlay networks for Web search, Information Processing and Management: an International Journal, v.43 n.3, p.643-664, May, 2007 [doi>10.1016/j.ipm.2006.09.007]
• 22: E. Parzen. On Estimation of a Probability Density Function and Mode. Ann. Math. Statist., 33:1065--1076, 1962.
• 23: W. M. Rand. Objective Criteria for the Evaluation of Clustering Methods. J. Amer. Stat. Assoc., 66(336):846--850, 1971.
• 24: Peter Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, v.20 n.1, p.53-65, Nov. 1987 [doi>10.1016/0377-0427(87)90125-7 ]
• 25: P. Triantafillou, C. Xiruhaki, M. Koubarakis, and N. Ntarmos. Towards High Performance Peer-to-Peer Content and Resource Sharing Systems. In Proc. of the 1st CIDR, 2003.
• 26: Cong Yu , H. V. Jagadish, Schema summarization, Proceedings of the 32nd international conference on Very large data bases, September 12-15, 2006, Seoul, Korea