Abstract
Internet users use the web to search for information they need. Every user has some particular interests and preferences when he/she searches information on the web. It is challenging to trace the exact interests of a user by a system to provide the information he/she wants. Personalization is a popular technique in information retrieval to present information based on an individual user’s needs. The main challenges of effective personalization are to model the users and identify the users’ context for accessing information. In this paper, we propose a framework to model the user details and context for personalized web search. We construct an ontological user profile describing the users preferences based on the users context. Finally, we use a semantic analysis of the log files approach for the initial construction of the ontological users profile and learn the profile over time. Web information can be accessed based on the ontological user profiles, re-ranking the searched results considering the users’ context. Experiments show that our ontological approach to modeling users and context enables us to tailor the web search results for users based on users’ interests and preferences.
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
Sarabjot, S.A., Patricia, K., Mary, S.: Generating Semantically Enriched User Profiles for web Personalization. ACM Transaction on Internet Technology 7(4) Article 22 (2007)
Carsten, F., Markus, L.: Ontology-Based User Profile. In: Abramowicz, W. (ed.) BIS 2007. LNCS, vol. 4439, pp. 314–327. Springer, Heidelberg (2007)
Hyunjang, K., Myunggowon, H., Pankoo, k.: Design of Automatic Ontology Building System about the Specific Domain Knowledge. In: ICACT, pp. 20–22 (2006)
Abhinandan, D., Mayur, D., Ashutosh, G.: Google News Personalization: Schable Online Collaborative Filtering. In: WWW 2007, pp. 8–12 (2007)
Trajkova, J., Gauch, S.: Improving Ontology-Based User Profiles. In: RIAO (2004)
Mylonas, P., Vallet, D., Castells, P., Fernsndez, M., Avrithis, Y.: Personalized information retrieval based on context and ontological knowledge. The Knowledge Engineering Review 23, 73–100 (2008)
Guha, R., McCool, R., Miller, E.: semantic search. In: WWW 2003, pp. 20–24 (May 2003)
Sieg, A., Mobasher, B., Burke, R.: Learning Ontology-Based User Profiles: A Semantic. Approach to Personalized Web Search. IEEE Intelligent Informatics Bulletin 8(1) (2007)
Gerald, S., Strembeck, M., Neumann, G.: A User Profile Derivation Approach based on Log-File Analysis, pp. 258–264. IKE, Las Vegas (June 2007)
Liu, F., Yu, C., Member, S., Meng, W.: Personalized Web Search For Improving Retrieval Effectiveness. IEEE Computer Society, Los Alamitos (2004)
Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: WWW 2006, Edinburg, UK (May 2006)
Hearst, M.A.: Automated Discovery of WordNet Relations. MIT Press, Cambridge
Vaclav, S., Pavel, M., Jaroslav, P.: Wordnet Ontology Based Model for Web Retrieval. In: WIRI 2005. IEEE, Los Alamitos (2005)
Maria, G., Akrivi, K., Costas, V., George, L., Constantin, H.: Creating an Ontology for the User Profile: Method and Applications. In: Proceedings AI*AI Workshop RCIS 2007, Italy (2002)
Trong, H.D., Uddin, M.N., Delong, L., Jo, G.S.: A collaborative Ontology-based Profiles system. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 540–552. Springer, Heidelberg (2009)
Susan, G., Jason, C., Alaxander, P.: Ontology-based personalized search and browsing. Web Intelligence and Agent Systems 1(3-4), 219–334 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mohammed, N.U., Duong, T.H., Jo, G.S. (2010). Contextual Information Search Based on Ontological User Profile. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16732-4_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-16732-4_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16731-7
Online ISBN: 978-3-642-16732-4
eBook Packages: Computer ScienceComputer Science (R0)