Abstract
Semantic mining is a research area that sprung up in the last decade. With the explosively growth of Linked Data, instance-focused Semantic Mining technologies now face the challenge of mining efficiency. In our observation, graph compression strategies can effectively reduce the redundant or dependent structures in Linked Data, thus can help to improve mining efficiency. In this paper, we first describe Typed Object Graph as a generic data model for instance-focused Semantic Mining; and then we propose two graph compression strategies for Linked Data: Equivalent Compression and Dependent Compression, each of which is demonstrated in specific mining scenarios. Experiments on real Linked Data show that graph compression strategies in Semantic Mining is feasible and effective for reducing the volume of Linked Data to improve mining efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Svatopluk, F., Ivan, J.: Semantic Mining of Web Documents. In: Proceedings of International Conference on Computer Systems and Technologies, pp. 21–26 (2005)
Zhang, X., Zhao, C., Wang, P., Zhou, F.: Mining Link Patterns in Linked Data. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds.) WAIM 2012. LNCS, vol. 7418, pp. 83–94. Springer, Heidelberg (2012)
Zhao, C.F., Zhang, X., Wang, P.: A Label-based Partitioning Strategy for Mining Link Patterns. In: Proceedings of 7th International Conference on Knowledge, Information and Creativity Support Systems, pp. 203–206 (2012)
Jiang, X.W., Zhang, X., Gui, W., Gao, F.F., Wang, P., Zhou, F.B.: Summarizing Semantic Associations Based on Focused Association Graph. In: Proceedings of the 8th International Comference, pp. 564–576 (2012)
Anyanwu, K., Sheth, A.: p-Queries: Enabling Querying for Semantic Associations on the Semantic Web. In: Proceedings of the 12th International World Wide Web Conference, pp. 690–699 (2003)
Sheth, A., Aleman-Meza, B., Arpina, I.B., et al.: Semantic Association Identifi-cation and Knowledge Discovery for National Security Applications. Journal of Database Management 16(1), 33–53 (2005)
Yan, X., Han, J.W.: gSpan: Graph-based Substructure Pattern Mining. In: Proceedings of the IEEE International Conference on Data Mining, pp. 721–724 (2002)
Yan, X., Han, J.W.: CloseGraph: Mining Closed Frequent Graph Patterns. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 286–295 (2003)
Hage, P., Harary, F.: Eccentricity and Centrality in Networks. Social Networks 17, 57–63 (1995)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford University (1998)
Kleinberg, J.: Authoritative Sources in a Hyperlinked Environment. In: Proceedings of the 9th ACM SIAM Symposium on Discrete Algorithms, pp. 668–677 (1998)
Chen, C., Lin, C.X., Fredrikson, M., Christodorescu, M., Yan, X.F., Han, J.W.: Mining Graph Patterns Efficiently via Randomized Summaries. In: Proceedings of the 35th International Conference on Very Large Data Bases, pp. 742–753 (2009)
Navlakha, S., Rastogi, R., Shrivastava, N.: Graph Summarization with Bounded Error. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 419–432 (2008)
Tian, Y., Hankins, R., Patel, J.: Efficient Aggregation for Graph Summarization. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 567–580 (2008)
Toivonen, H., Zhou, F., Hartikainen, A., Hinkka, A.: Network Compression by Node and Edge Mergers. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS, vol. 7250, pp. 199–217. Springer, Heidelberg (2012)
Toivonen, H., Zhou, F., Hartikainen, A., Hinkka, A.: Compression of Weighted Graphs. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 965–973 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jiang, X., Zhang, X., Gao, F., Pu, C., Wang, P. (2013). Graph Compression Strategies for Instance-Focused Semantic Mining. In: Qi, G., Tang, J., Du, J., Pan, J.Z., Yu, Y. (eds) Linked Data and Knowledge Graph. CSWS 2013. Communications in Computer and Information Science, vol 406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54025-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-54025-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54024-0
Online ISBN: 978-3-642-54025-7
eBook Packages: Computer ScienceComputer Science (R0)