Advertisement

Mining Link Patterns in Linked Data

  • Xiang Zhang
  • Cuifang Zhao
  • Peng Wang
  • Fengbo Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7418)

Abstract

As the explosive growth of online linked data, an emerging problem is what and how we can learn from these data. An important knowledge we can obtain is the link patterns among objects, which are helpful for characterizing, analyzing and understanding of linked data. In this paper, we present a novel approach of mining link patterns. A Typed Object Graph is proposed as the data model, and a gSpan-based algorithm is proposed for pattern mining. A type determination policy is introduced in cases of multi-types and a data clustering algorithm is proposed to improve scalability. Time performance and mining results are discussed by experiments.

Keywords

linked data frequent link pattern semantic web 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sheth, A., Aleman-Meza, B., Arpinar, B., et al.: Semantic Association Identification and Knowledge Discovery for National Security Applications. Journal of Database Management 16(1), 33–53 (2005)CrossRefGoogle Scholar
  2. 2.
    Basse, A., Gandon, F., Mirbel, I., et al.: DFS-based Frequent Graph Pattern Extraction to Characterize the Content of RDF Triple Stores. In: Proceedings of the WebSci1 2010: Extending the Frontiers of Society Online (2010)Google Scholar
  3. 3.
    Thor, A., Anderson, P., Raschid, L., Navlakha, S., Saha, B., Khuller, S., Zhang, X.-N.: Link Prediction for Annotation Graphs Using Graph Summarization. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 714–729. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Dai, H., Mobasher, B.: Integrating Semantic Knowledge with Web Usage Mining for Personalization. In: Web Mining: Applications and Techniques, pp. 273–306 (2004)Google Scholar
  5. 5.
    Xu, X., Cong, G., Ooi, B.C., et al.: Semantic Mining and Analysis of Gene Expression Data. In: Proceedings of the 30th International Conference on Very Large Data Bases, pp. 1261–1264 (2004)Google Scholar
  6. 6.
    Yan, X., Han, J.W.: gSpan: Graph-based Substructure Pattern Mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 721–724 (2002)Google Scholar
  7. 7.
    Hayes, P.: RDF Semantics. W3C Recommendation (February 10, 2004), http://www.w3.org/TR/rdf-mt/
  8. 8.
    Cheng, G., Qu, Y.: Integrating Lightweight Reasoning into Class-Based Query Refinement for Object Search. In: Domingue, J., Anutariya, C. (eds.) ASWC 2008. LNCS, vol. 5367, pp. 449–463. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Maedche, A., Zacharias, V.: Clustering Ontology-Based Metadata in the Semantic Web. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 348–360. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Grimnes, G.A., Edwards, P., Preece, A.D.: Instance Based Clustering of Semantic Web Resources. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 303–317. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Penin, T., Wang, H., Tran, T., Yu, Y.: Snippet Generation for Semantic Web Search Engines. In: Domingue, J., Anutariya, C. (eds.) ASWC 2008. LNCS, vol. 5367, pp. 493–507. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Patel, C., Supekar, K., Lee, Y., Park, E.K.: OntoKhoj: A Semantic Web Portal for Ontology Searching, Ranking and Classification. In: Proceedings of 5th ACM International Workshop on Web Information and Data Management, pp. 58–61 (2003)Google Scholar
  13. 13.
    Seidenberg, J., Rector, A.: Web Ontology Segmentation: Analysis, Classification and Use. In: Proceedings of 15th International Word Wide Web Conference, pp. 13–22 (2006)Google Scholar
  14. 14.
    Han, J.W., Kamber, M.: Data Mining Concepts and Techniques, 2nd edn. Elsevier Inc. (2006)Google Scholar
  15. 15.
    Yan, X., Han, J.W.: CloseGraph: Mining Closed Frequent Graph Patterns. In: Proceedings of the 9th ACM SIGKDD Internal Conference on Knowledge Discovery and Data Mining, pp. 285–295 (2003)Google Scholar
  16. 16.
    Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiang Zhang
    • 1
  • Cuifang Zhao
    • 1
  • Peng Wang
    • 1
  • Fengbo Zhou
    • 2
  1. 1.School of Computer Science and EngineeringSoutheast UnivesityNanjingChina
  2. 2.Focus Technology Co., Ltd.NanjingChina

Personalised recommendations