Skip to main content

Ontology-Based Filtering Mechanisms for Web Usage Patterns Retrieval

  • Conference paper
E-Commerce and Web Technologies (EC-Web 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3590))

Included in the following conference series:

Abstract

Web Usage Mining (WUM) aims to extract navigation usage patterns from Web server logs. Mining algorithms yield usage patterns, but finding the ones that constitute new and interesting knowledge in the domain remains a challenge. Typically, analysts have to deal with a huge volume of pattern, from which they have to retrieve the potentially interesting one and interpret what they reveal about the domain. In this paper, we discuss the filtering mechanisms of O3R, an environment supporting the retrieval and interpretation of sequential navigation patterns. All O3R functionality is based on the availability of the domain ontology, which dynamically provides meaning to URLs. The analyst uses ontology concepts to define filters, which can be applied according to two filtering mechanisms: equivalence and similarity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cooley, R.: The use of web structure and content to identify subjectively interesting web usage patterns. ACM Transactions on Internet Technology 3(2), 93–116 (2003)

    Article  Google Scholar 

  2. Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems 1, 5–32 (1999)

    Google Scholar 

  3. Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering 8(6), 970–974 (1996)

    Article  Google Scholar 

  4. Stumme, G., Hotho, A., Berendt, B.: Usage mining for and on the semantic web. In: National Science Foundation Workshop on Next Generation Data Mining, Baltimore, USA (2002)

    Google Scholar 

  5. Vanzin, M., Becker, K.: Exploiting knowledge representation for pattern interpretation. In: Workshop on Knowledge Discovery and Ontologies- KDO 2004, Pisa, Italy, pp. 61–71 (2004)

    Google Scholar 

  6. Oberle, D., Berendt, B., Hotho, A., Gonzalez, J.: Conceptual user tracking. In: Menasalvas, E., Segovia, J., Szczepaniak, P.S. (eds.) AWIC 2003. LNCS (LNAI), vol. 2663, pp. 142–154. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  7. Dai, H., Mobasher, B.: Using ontologies to discovery domain-level web usage profiles. In: Semantic Web Mining Workshop (2002)

    Google Scholar 

  8. Berendt, B., Spiliopoulou, M.: Analysing navigation behaviour in web sites integrating multiple information systems. The VLDB Journal 9, 56–75 (2000)

    Article  Google Scholar 

  9. Hipp, J., Guntzer, U.: Is pushing constraints deeply into the mining algorithms really what we want? an alternative approach for association rule mining. SIGKDD Explor. Newsl. 4(1), 50–55 (2002)

    Article  Google Scholar 

  10. Agrawal, R., Srikant, R.: Mining sequential patterns. In: 11th International Conference on Data Engineering, pp. 3–14 (1995)

    Google Scholar 

  11. Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding interesting rules from large sets of discovered association rules. In: 3rd Iinternational Conference on Information and Knowledge Management, pp. 401–407 (1994)

    Google Scholar 

  12. Sure, Y., Angele, J., Staab, S.: Ontoedit: Guiding ontology development by methodology and inferencing. In: International Conference on Ontologies, Databases and Applications of SEmantics ODBASE, pp. 1205–1222 (2002)

    Google Scholar 

  13. Ganesan, P., Garcia-Molina, H., Widom, J.: Exploiting hierarchical domain structure to compute similarity. ACM Transactions on Information Systems 21(1), 64–93 (2003)

    Article  Google Scholar 

  14. Machado, L., Becker, K.: Distance education: a web usage mining case study for the evaluation of learning sites. In: 3rd IEEE International Conference on Advanced Learning Technologies, pp. 360–371 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vanzin, M., Becker, K., Ruiz, D.D.A. (2005). Ontology-Based Filtering Mechanisms for Web Usage Patterns Retrieval. In: Bauknecht, K., Pröll, B., Werthner, H. (eds) E-Commerce and Web Technologies. EC-Web 2005. Lecture Notes in Computer Science, vol 3590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11545163_27

Download citation

  • DOI: https://doi.org/10.1007/11545163_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28467-3

  • Online ISBN: 978-3-540-31736-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics