Skip to main content
SpringerLink
Log in

Improving pattern quality in web usage mining by using semantic information

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Frequent Web navigation patterns generated by using Web usage mining techniques provide valuable information for several applications such as Web site restructuring and recommendation. In conventional Web usage mining, semantic information of the Web page content does not take part in the pattern generation process. In this work, we investigate the effect of semantic information on the patterns generated for Web usage mining in the form of frequent sequences. To this aim, we developed a technique and a framework for integrating semantic information into Web navigation pattern generation process, where frequent navigational patterns are composed of ontology instances instead of Web page addresses. The quality of the generated patterns is measured through an evaluation mechanism involving Web page recommendation. Experimental results show that more accurate recommendations can be obtained by including semantic information in navigation pattern generation, which indicates the increase in pattern quality.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abraham A, Ramos V (2003) Web usage mining using artificial ant colony clustering and linear genetic programming. In: Proceedings of congress on evolutionary computation (CEC), pp 1384–1391

  2. Adomavicius G, Tuzhilin E (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6): 734–749

    Article  Google Scholar 

  3. Baumgarten M, Buchner AG, Anand SS, Mulvenna MD, Hughes JG (2000) User-driven navigation pattern discovery from internet data. In: Proceedings of workshop on web usage analysis and user profiling, pp 74–91

  4. Becchetti L, Colesanti U, Marchetti-Spaccamela A, Vitaletti A (2010) Recommending items in pervasive scenarios: models and experimental analysis. Knowl Inf Syst, September [Online]

  5. Berendt B, Hotho A, Stumme G (2002) Towards semantic web mining. In: Proceedings of international semantic web conference (ISWC), pp 264–278. Springer, Berlin

  6. Bezerra BLD, Carvalho FAT (2010) Symbolic data analysis tools for recommendation systems. Knowl Inf Syst, February [Online]

  7. Borges JL, Levene M (2000) Data mining of user navigation patterns. In: Proceedings of workshop on web usage analysis and user profiling, pp 31–36

  8. Britos P, Martinelli D, Merlino H, Garcia-Martinez R (2007) Web usage mining using self organized maps. Int J Comput Sci Netw Secur 7(6): 45–50

    Google Scholar 

  9. Cho WC, Richards D (2004) Improvement of precision and recall for information retrieval in a narrow domain: Reuse of concepts by formal concept analysis. In: Proceedings of the 2004 IEEE/WIC/ACM international conference on web intelligence, pp 370–376, Washington, 2004. IEEE Computer Society

  10. Cho WC, Richards D (2007) Ontology construction and concept reuse with formal concept analysis for improved web document retrieval. Web Intell Agent Syst 5(1): 109–126

    Google Scholar 

  11. Dai H, Mobasher B (2005) Integrating semantic knowledge with web usage mining for personalization. In: Scime A, (eds) Web mining: applications and techniques. IRM Press, Idea Group Publishing

  12. Daoud M, Lechani L, Boughanem M (2009) Towards a graph-based user profile modeling for a session-based personalized search. Knowl Inf Syst 21(3): 365–398

    Article  Google Scholar 

  13. Hay B, Wets G, Vanhoof K (2004) Mining navigation patterns using a sequence alignment method. Knowl Inf Syst 6(2): 150–163

    Google Scholar 

  14. Masseglia l, Teisseire M, Poncelet P (2003) HDM: a client/server/engine architecture for real-time web usage mining. Knowl Inf Syst 5(4): 439–465

    Article  Google Scholar 

  15. Leung CW, Chan SC, Chung F (2006) A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl Inf Syst 10(3): 357–381

    Article  Google Scholar 

  16. Mabroukeh NR, Ezeife CI (2009) Using domain ontology for semantic web usage mining and next page prediction. In: Proceedings of conference on information and knowledge management (CIKM), pp 1677–1680

  17. Missaoui R, Valtchev P, Djeraba C, Adda M (2007) Toward recommendation based on ontology-powered web-usage mining. IEEE Internet Comput 11(4): 45–52

    Article  Google Scholar 

  18. Mobasher B, Cooley R, Srivastava J (2000) Automatic personalization based on web usage mining. Commun ACM 43(8): 142–151

    Article  Google Scholar 

  19. Mobasher B, Dai H, Luo T, Sun Y, Zhu J (2000) Integrating web usage and content mining for more effective personalization. In: Proceedings of international conference on E-Commerce and web technologies (ECWeb2000), pp 165–176, Greenwich, UK

  20. Nakagawa M, Mobasher B (2003) Impact of site characteristics on recommendation models based on association rules and sequential patterns. In: Proceedings of IJCAI’03 workshop on intelligent techniques for web personalization, Acapulco, Mexico, August

  21. Nasraoui O, Soliman M, Saka E, Badia A, Germain R (2008) A web usage mining framework for mining evolving user profiles in dynamic web sites. IEEE Trans Knowl Data Eng 20(2): 202–215

    Article  Google Scholar 

  22. Park S, Suresh NC, Jeong B (2008) Sequence-based clustering for web usage mining: a new experimental framework and ann-enhanced k- means algorithm. Data Knowl Eng 65(3): 512–543

    Article  Google Scholar 

  23. Richards D (2004) Addressing the ontology acquisition bottleneck through reverse ontological engineering. Knowl Inf Syst 6(4): 402–427

    Article  MathSciNet  Google Scholar 

  24. Rohn E (2010) Generational analysis of variety in data structures: impact on automatic data integration and on the semantic web. Knowl Inf Syst 24(2): 283–304

    Article  MathSciNet  Google Scholar 

  25. Salin S, Senkul P (2009) Using semantic information for web usage mining based recommendation. In: Proceedings of international symposium on computer and information sciences (ISCIS 09), pp 236–241, September

  26. Shchekotykhin K, Jannach D, Friedrich G (2009) xCrawl: A high-recall crawling method for web mining. Knowl Inf Syst, November [Online]

  27. Shyu M, Haruechaiyasak C, Chen S (2006) Mining user access patterns with traversal constraint for predicting web page requests. Knowl Inf Syst 10(4): 515–528

    Article  Google Scholar 

  28. Spiliopoulou M (1999) The laborious way from data mining to web mining. Int J Comput Syst Sci Eng 14: 113–126

    Google Scholar 

  29. Spiliopoulou M, Pohle C (2001) Data mining for measuring and improving the success of web sites. Data Mining Knowl Discov 5(1–2): 14–85

    Google Scholar 

  30. Spiliopoulou M, Pohle C, Teltzrow M (2002) Modelling and mining web site usage strategies. In: Proceedings of multi-konferenz wirtschaftsinformatik, September

  31. Srikant R, Agrawal R (1995) Mining generalized association rules. In: Proceedings of international conference on very large databases (VLDB), pp 407–419, Zurich, Switzerland, September

  32. Srikant R, Agrawal R (1996) Mining sequential patterns: Generalizations and performance improvements. In: Proceedings of international conference on extending database technology (EDBT) pp 3–17, France, March

  33. Stumme G, Berendt B, Hotho A (2002) Usage mining for and on the semantic web: next generation data mining. In: Proceedings of NSF workshop, pp 77–86, Baltimore, November

  34. Stumme G, Hotho A, Berendt B (2006) Semantic web mining: state of the art and future directions. J Web Semant Sci Serv Agents World Wide Web 4(2): 124–143

    Article  Google Scholar 

  35. Tan A, Ong H, Pan H, Ng J, Qiu-Xiang Li (2006) Towards personalised web intelligence. Knowl Inf Syst 6(5): 595–616

    Google Scholar 

  36. Vucetic S, Obradovaic Z (2005) Collaborative filtering using a regression-based approach. Knowl Inf Syst 7(1): 1–22

    Article  Google Scholar 

  37. Wang W, Zaine OR (2002) Clustering web sessions by sequence alignment. In: Proc. of the 13th International Workshop on Database and Expert Systems Applications (DEXA 2002), pp 394–398, Aix-en-Provence

  38. World Wide Web Consortium (W3C). Web ontology language(OWL)

  39. Yilmaz H, Senkul P (2010) Using ontology and sequence information for extracting behavior patterns from web navigation logs. In: Proceedings of IEEE ICDM workshop on semantic aspects in data mining (SADM’10), pp 549–556, December

  40. Zaki MJ (2000) Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 12(3): 372–390

    Article  MathSciNet  Google Scholar 

  41. Zhou B, Hui SC, Fong ACM (2005) Web usage mining for semantic web personalization. In: Proceedings of workshop on personalization on the semantic web (PerSWeb), pp 66–72, July

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Middle East Technical University, 06531, Ankara, Turkey

    Pinar Senkul & Suleyman Salin

Authors
  1. Pinar Senkul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suleyman Salin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pinar Senkul.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Senkul, P., Salin, S. Improving pattern quality in web usage mining by using semantic information. Knowl Inf Syst 30, 527–541 (2012). https://doi.org/10.1007/s10115-011-0386-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-011-0386-4

Keywords

Search

Navigation