Abstract
Current conventional search engines deliver similar results to all users for the same query. Because of the variety of user interests and preferences, personalized search engines, based on semantics, hold the promise of providing more efficient information that better reflects users’ needs. The main feature of building a personalized web search is to represent user interests in terms of user profiles. This paper proposes a personalized search approach using an ontology-based user profile. The aim of this approach is to build user profiles based on user browsing behavior and semantic knowledge of specific domain ontology to enhance the quality of the search results. The proposed approach utilizes a re-ranked algorithm to sort the results returned by the search engine to provide a search result that best relates to the user query. This algorithm evaluates the similarity between a user query, the retrieved search results and the ontological concepts. This similarity is computed by taking into account a user’s explicit browsing behavior, semantic knowledge of concepts, and synonyms of term-based vectors extracted from the WordNet API. A set of experiments using a case study from a transport service domain validates the effectiveness of the proposed approach and demonstrates promising results.
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
Similar content being viewed by others
References
Sieg, A., Mobasher, B., Burke, R.D.: Learning ontology-based user profiles: a semantic approach to personalized web search. IEEE Intell. Inform. Bull. 8, 7–18 (2007)
Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72079-9_2
Akhlaghian, F., Arzanian, B., Moradi, P.: A personalized search engine using ontology-based fuzzy concept networks. In: The International Conference on Data Storage and Data Engineering, pp. 137–141. IEEE, Bangalore (2010)
Baazaoui-Zghal, H., Ghezala, H.B.: A fuzzy-ontology-driven method for a personalized query reformulation. In: The IEEE International Conference on Fuzzy Systems, pp. 1640–1647. IEEE Press, Beijing (2014)
Daoud, M., Tamine-Lechani, L., Boughanem, M.: Using a concept-based user context for search personalization. In: Proceedings of the 2008 International Conference of Data Mining and Knowledge Engineering, London (2008)
Ferreira-Satler, M., Romero, F.P., Menendez-Dominguez, V.H., Zapata, A., Prieto, M.E.: Fuzzy ontologies-based user profiles applied to enhance e-learning activities. Soft. Comput. 16, 1129–1141 (2012)
Nanda, A., Omanwar, R., Deshpande, B.: Implicitly learning a user interest profile for personalization of web search using collaborative filtering. In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 2, pp. 54–62 (2014)
Jiang, X., Tan, A.H.: Learning and inferencing in user ontology for personalized Semantic Web search. Inf. Sci. 179, 2794–2808 (2009)
Hourali, M., Montazer, G.A.: An intelligent information retrieval approach based on two degrees of uncertainty fuzzy ontology. Adv. Fuzzy Syst. 2011, 7 (2011)
Al-Hassan, M., Lu, H., Lu, J.: A semantic enhanced hybrid recommendation approach: a case study of e-Government tourism service recommendation system. Decis. Support Syst. 72, 97–109 (2015)
Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S.: Personalized search on the world wide web. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 195–230. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72079-9_6
Duong, T.H., Uddin, M.N., Nguyen, C.D.: Personalized semantic search using ODP: a study case in academic domain. In: Murgante, B., Misra, S., Carlini, M., Torre, Carmelo M., Nguyen, H.-Q., Taniar, D., Apduhan, Bernady O., Gervasi, O. (eds.) ICCSA 2013. LNCS, vol. 7975, pp. 607–619. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39640-3_44
Calegari, S., Pasi, G.: Ontology-based information behaviour to improve web search. Future Internet 2, 533–558 (2010)
Baazaoui, H., Aufaure, M.A., Soussi, R., Laboratoy, R.G., de la Manouba, E.C.U.: Towards an on-line semantic information retrieval system based on fuzzy ontologies. J. Digital Inf. Manag. 6, 375 (2008)
Sánchez, D., Batet, M., Isern, D., Valls, A.: Ontology-based semantic similarity: a new feature-based approach. Expert Syst. Appl. 39, 7718–7728 (2012)
Dong, H., Hussain, F.K., Chang, E.: A service search engine for the industrial digital ecosystems. IEEE Trans. Ind. Electron. 58, 2183–2196 (2011)
Porter, M.F.: An algorithm for suffix stripping. Program 14, 130–137 (1980)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
ElShaweesh, O., Hussain, F.K., Lu, H., Al-Hassan, M., Kharazmi, S. (2017). Personalized Web Search Based on Ontological User Profile in Transportation Domain. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-70093-9_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70092-2
Online ISBN: 978-3-319-70093-9
eBook Packages: Computer ScienceComputer Science (R0)