Personalized Web Search Based on Ontological User Profile in Transportation Domain

  • Omar ElShaweeshEmail author
  • Farookh Khadeer Hussain
  • Haiyan Lu
  • Malak Al-Hassan
  • Sadegh Kharazmi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)


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.


Fuzzy Personalization Ontology Web search User profile 


  1. 1.
    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)Google Scholar
  2. 2.
    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 CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Jiang, X., Tan, A.H.: Learning and inferencing in user ontology for personalized Semantic Web search. Inf. Sci. 179, 2794–2808 (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Hourali, M., Montazer, G.A.: An intelligent information retrieval approach based on two degrees of uncertainty fuzzy ontology. Adv. Fuzzy Syst. 2011, 7 (2011)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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 CrossRefGoogle Scholar
  12. 12.
    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 CrossRefGoogle Scholar
  13. 13.
    Calegari, S., Pasi, G.: Ontology-based information behaviour to improve web search. Future Internet 2, 533–558 (2010)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    Sánchez, D., Batet, M., Isern, D., Valls, A.: Ontology-based semantic similarity: a new feature-based approach. Expert Syst. Appl. 39, 7718–7728 (2012)CrossRefGoogle Scholar
  16. 16.
    Dong, H., Hussain, F.K., Chang, E.: A service search engine for the industrial digital ecosystems. IEEE Trans. Ind. Electron. 58, 2183–2196 (2011)CrossRefGoogle Scholar
  17. 17.
    Porter, M.F.: An algorithm for suffix stripping. Program 14, 130–137 (1980)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Omar ElShaweesh
    • 1
    Email author
  • Farookh Khadeer Hussain
    • 1
  • Haiyan Lu
    • 1
  • Malak Al-Hassan
    • 2
  • Sadegh Kharazmi
    • 3
  1. 1.University of TechnologySydneyAustralia
  2. 2.The University of JordanAmmanJordan
  3. 3.RedbubbleMelbourneAustralia

Personalised recommendations