Advertisement

Introducing Semantics in Web Personalization: The Role of Ontologies

  • Magdalini Eirinaki
  • Dimitrios Mavroeidis
  • George Tsatsaronis
  • Michalis Vazirgiannis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4289)

Abstract

Web personalization is the process of customizing a web site to the needs of each specific user or set of users. Personalization of a web site may be performed by the provision of recommendations to the users, high-lighting/adding links, creation of index pages, etc. The web personalization systems are mainly based on the exploitation of the navigational patterns of the web site’s visitors. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally recommended may be missed. The exploitation of the web pages’ semantics can considerably improve the results of web usage mining and personalization, since it provides a more abstract yet uniform and both machine and human understandable way of processing and analyzing the usage data. The underlying idea is to integrate usage data with content semantics, expressed in ontology terms, in order to produce semantically enhanced navigational patterns that can subsequently be used for producing valuable recommendations. In this paper we propose a semantic web personalization system, focusing on word sense disambiguation techniques which can be applied in order to semantically annotate the web site’s content.

Keywords

Association Rule Frequent Itemsets Ontology Term Document Cluster Word Sense Disambiguation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agirre, E., Rigau, G.: A proposal for word sense disambiguation using conceptual distance. In: Proc. of Recent Advances in NLP (RANLP), 258–264 (1995)Google Scholar
  2. 2.
    Acharyya, S., Ghosh, J.: Context-Sensitive Modeling of Web Surfing Behaviour Using Concept Trees. In: Proc. of the 5th WEBKDD Workshop, Washington (August 2003)Google Scholar
  3. 3.
    Albanese, M., Picariello, A., Sansone, C., Sansone, L.: A Web Personalization System based on Web Usage Mining Techniques. In: Proc. of WWW 2004, New York, USA (May 2004)Google Scholar
  4. 4.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of 20th VLDB Conference (1994)Google Scholar
  5. 5.
    Barzilay, R., Elhadad, M.: Using lexical chains for text summarization. In: Proc. of the Intelligent Scalable Text Summarization Workshop (ISTS 1997), ACL (1997)Google Scholar
  6. 6.
    Berendt, B., Hotho, A., Stumme, G.: Towards Semantic Web Mining. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342. Springer, Heidelberg (2002)Google Scholar
  7. 7.
    Bloehdorn, S., Hotho, A.: Boosting for text classification with semantic features. In: Proc. of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Mining for and from the Semantic Web Workshop, pp. 70–87 (2004)Google Scholar
  8. 8.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks 30(1-7), 107–117 (1998); Proc. of the 7th International World Wide Web Conference (WWW7)Google Scholar
  9. 9.
    Baraglia, R., Silvestri, F.: An Online Recommender System for Large Web Sites. In: Proc. of ACM/IEEE Web Intelligence Conference (WI 2004), China (September 2004)Google Scholar
  10. 10.
    Chakrabarti, S., Dom, B., Raghavan, P., Rajagopalan, S., Gibson, D., Kleinberg, J.: Automatic Resource Compilation by Analyzing Hyperlink Structure and Associated Text. In: Proc. of WWW7 (1998)Google Scholar
  11. 11.
    Dai, H., Mobasher, B.: Using Ontologies to Discover Domain-Level Web Usage Profiles. In: Proc. of the 2nd Workshop on Semantic Web Mining, Helsinki, Finland (2002)Google Scholar
  12. 12.
    Eirinaki, M.: New Approaches to Web Personalization, PhD Thesis, Athens University of Economics and Business, Dept. of Informatics (2006)Google Scholar
  13. 13.
    Ester, M., Kriegel, H.P., Sander, J., Wimmer, M., Xu, X.: Incremental Clustering for Mining in a Data Warehousing Environment. In: Proc. of the 24th VLDB Conference (1998)Google Scholar
  14. 14.
    Eirinaki, M., Lampos, C., Pavlakis, S., Vazirgiannis, M.: Web Personalization Integrating Content Semantics and Navigational Patterns. In: Proc. of the 6th ACM International Workshop on Web Information and Data Management (WIDM 2004), Washington DC (November 2004)Google Scholar
  15. 15.
    Eirinaki, M., Vazirgiannis, M., Varlamis, I.: SEWeP: Using Site Semantics and a Taxonomy to Enhance the Web Personalization Process. In: Proc. of the 9th ACM International Conference on Knowledge Discovery and Data Mining (KDD 2003), Washington DC (August 2003)Google Scholar
  16. 16.
    Fellbaum, C. (ed.): WordNet, An Electronic Lexical Database. The MIT Press, Cambridge (1998)MATHGoogle Scholar
  17. 17.
    Galley, M., McKeown, K.: Improving Word Sense Disambiguation in Lexical Chaining. In: Proc. of the 18th International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco, Mexico (August 2003)Google Scholar
  18. 18.
    Guo, J., Keselj, V., Gao, Q.: Integrating Web Content Clustering into Web Log Association Rule Mining. In: Proc. of Canadian AI Conference 2005 (2005)Google Scholar
  19. 19.
    Halkidi, M., Nguyen, B., Varlamis, I., Vazirgiannis, M.: THESUS: Organizing Web Documents into Thematic Subsets using an Ontology. VLDB journal 12(4), 320–332 (2003)CrossRefGoogle Scholar
  20. 20.
    Hotho, A., Staab, S., Stumme, G.: Ontologies Improve Text Document Clustering. In: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), Melbourne, Florida, USA, December 19-22, pp. 541–544 (2003)Google Scholar
  21. 21.
    Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proc. of the International Conference on Research in Computational Linguistics (1997)Google Scholar
  22. 22.
    Jin, X., Zhou, Y., Mobasher, B.: A Maximum Entropy Web Recommendation System: Combining Collaborative and Content Features. In: Proc. of the 11th ACM International Conference on Knowledge Discovery and Data Mining (KDD 2005), Chicago (August 2005)Google Scholar
  23. 23.
    Kearney, P., Anand, S.S.: Employing a Domain Ontology to gain insights into user behaviour. In: Proc. of the 3rd Workshop on Intelligent Techniques for Web Personalization (ITWP 2005), Endiburgh, Scotland (August 2005)Google Scholar
  24. 24.
    Leacock, C., Chodorow, M.: Combining Local Context and WordNet Similarity for Word Sense Identification. In: Fellbaum 1998, pp. 265–283 (1998)Google Scholar
  25. 25.
    Leacock, C., Chodorow, M., Miller, G.A.: Using Corpus Statistics and WordNet Relations for Sense Identification. Computational Linguistics 24(1), 147–165 (1998)Google Scholar
  26. 26.
    Lampos, C., Eirinaki, M., Jevtuchova, D., Vazirgiannis, M.: Archiving the Greek Web. In: Proc. of the 4th International Web Archiving Workshop (IWAW 2004), Bath, UK (September 2004)Google Scholar
  27. 27.
    Lesk, M.E.: Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In: Proc. of the SIGDOC Conference, Toronto (June 1986)Google Scholar
  28. 28.
    Lin, D.: An information-theoretic definition of similarity. In: Proc. of the 15th International Conference on Machine Learning (ICML), pp. 296–304 (1998)Google Scholar
  29. 29.
    Mavroeidis, D., Tsatsaronis, G., Vazirgiannis, M., Theobald, M., Weikum, G.: Word Sense Disambiguation for Exploiting Hierarchical Thesauri in Text Classification. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  30. 30.
    Mobasher, B., Dai, H., Luo, T., Sung, Y., Zhu, J.: Integrating web usage and content mining for more effective personalization. In: Proc. of the International Conference on Ecommerce and Web Technologies (ECWeb 2000), Greenwich, UK (September 2000)Google Scholar
  31. 31.
    Montoyo, A., Suarez, A., Rigau, G., Palomar, M.: Combining Knowledge- and Corpus-based Word-Sense-Disambiguation Methods. Journal of Artificial Intelligence Research 23, 299–330Google Scholar
  32. 32.
    Mihalcea, R., Moldovan, D.I.: A Highly Accurate Bootstrapping Algorithm for Word Sense Disambiguation. International Journal on Artificial Intelligence Tools 10(1-2), 5–21 (2001)CrossRefGoogle Scholar
  33. 33.
    Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological User Profiling in Recommender Systems. ACM Transactions on Information Systems (TOIS) 22(1), 54–88 (2004)CrossRefGoogle Scholar
  34. 34.
    Mihalcea, R., Tarau, P., Figa, E.: Pagerank on semantic networks, with application to word sense disambiguation. In: Proc. of the 20th International Conference on Computational Linguistics (COLING 2004), Switzerland (August 2004)Google Scholar
  35. 35.
    Navigli, R., Velardi, P.: Structural Semantic Interconnection: a knowledge-based approach to Word Sense Disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TRAMI) 27(7), 1075–1086Google Scholar
  36. 36.
    Oberle, D., Berendt, B., Hotho, A., Gonzalez, J.: Conceptual User Tracking. In: Proc. of the 1st Atlantic Web Intelligence Conf. (AWIC) (2003)Google Scholar
  37. 37.
    Perkowitz, M., Etzioni, O.: Towards Adaptive Web Sites: Conceptual Framework and Case Study. Artificial Intelligence 118(1-2), 245–275 (2000)MATHCrossRefGoogle Scholar
  38. 38.
    Paulakis, S., Lampos, C., Eirinaki, M., Vazirgiannis, M.: SEWeP: A web mining system supporting semantic personalization. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS, vol. 3202, pp. 552–554. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  39. 39.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proc. of the 14th International Joint Conference on Artificial Intelligence (IJCAI) (1995)Google Scholar
  40. 40.
    Rigau, G., Atserias, J., Agirre, E.: Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation. In: Proc. of joint 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, Madrid, Spain (1997)Google Scholar
  41. 41.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proc. of WWW 10, Hong Kong (May 2001)Google Scholar
  42. 42.
    Silber, G., McCoy, K.: Efficiently computed lexical chains as an intermediate representation for automatic text summarization. Computational Linguistics 29(1) (2003)Google Scholar
  43. 43.
    Sussna, M.: Word sense disambiguation for free-text indexing using a massive semantic network. In: Proc. of the 2nd International Conference on Information and Knowledge Management (CIKM), pp. 67–74 (1993)Google Scholar
  44. 44.
    Theobald, M., Schenkel, R., Weikum, G.: Exploiting structure, annotation, and ontological knowledge for automatic classification of xml data. In: International Workshop on Web and Databases (WebDB), pp. 1–6 (2003)Google Scholar
  45. 45.
    Utard, H., Furnkranz, J.: Link-Local Features for Hypertext Classification. In: Ackermann, M., Berendt, B., Grobelnik, M., Hotho, A., Mladenič, D., Semeraro, G., Spiliopoulou, M., Stumme, G., Svátek, V., van Someren, M. (eds.) EWMF 2005 and KDO 2005. LNCS, vol. 4289, pp. 51–64. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  46. 46.
    Varlamis, I., Vazirgiannis, M., Halkidi, M., Nguyen, B.: THESUS, A Closer View on Web Content Management Enhanced with Link Semantics. IEEE Transactions on Knowledge and Data Engineering Journal 16(6), 585–600 (2004)Google Scholar
  47. 47.
    Wong, S.K.M., Ziarko, W., Wong, P.C.N.: Generalized vector space model in information retrieval. In: Proc. of the 8th Intl. ACM SIGIR Conference (SIGIR 1985), pp. 18–25 (1985)Google Scholar
  48. 48.
    Wu, Z., Palmer, M.: Verb Semantics and Lexical Selection. In: 32nd Annual Meetings of the Associations for Computational Linguistic, pp. 133–138 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Magdalini Eirinaki
    • 1
  • Dimitrios Mavroeidis
    • 1
  • George Tsatsaronis
    • 1
  • Michalis Vazirgiannis
    • 1
  1. 1.Dept. of InformaticsAthens University of Economics and BusinessAthensGreece

Personalised recommendations