Personalized Concept-Based Search and Exploration on the Web of Data Using Results Categorization

  • Melike Sah
  • Vincent Wade
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)


As the size of the Linked Open Data (LOD) increases, searching and exploring LOD becomes more challenging. To overcome this issue, we propose a novel personalized search and exploration mechanism for the Web of Data (WoD) based on concept-based results categorization. In our approach, search results (LOD resources) are conceptually categorized into UMBEL concepts to form concept lenses, which assist exploratory search and browsing. When the user selects a concept lens for exploration, results are immediately personalized. In particular, all concept lenses are personally re-organized according to their similarity to the selected concept lens using a similarity measure. Within the selected concept lens; more relevant results are included using results re-ranking and query expansion, as well as relevant concept lenses are suggested to support results exploration. This is an innovative feature offered by our approach since it allows dynamic adaptation of results to the user’s local choices. We also support interactive personalization; when the user clicks on a result, within the interacted lens, relevant categories and results are included using results re-ranking and query expansion. Our personalization approach is non-intrusive, privacy preserving and scalable since it does not require login and implemented at the client-side. To evaluate efficacy of the proposed personalized search, a benchmark was created on a tourism domain. The results showed that the proposed approach performs significantly better than a non-adaptive baseline concept-based search and traditional ranked list presentation.


Concept-based search personalized search/exploration linked open data UMBEL query expansion results re-ranking interactive personalization 


  1. 1.
    Delbru, R., Campinas, S., Tummarello, G.: Searching Web Data: An Entity Retrieval and High-Performance Indexing Model. Journal of Web Semantics 10, 33–58 (2012)CrossRefGoogle Scholar
  2. 2.
    D’Aquin, M., Motta, E., Sabou, M., Angeletou, S., Gridinoc, L., Lopez, V., Guidi, D.: Toward a New Generation of Semantic Web Applications. IEEE Intelligent Systems (2008)Google Scholar
  3. 3.
    Tummarello, G., Cyganiak, R., Catasta, M., Danielczyk, S., Delbru, R., Decker, S.: Live views on the Web of Data. Journal of Web Semantics 8(4), 355–364 (2010)CrossRefGoogle Scholar
  4. 4.
    Heim, P., Ertl, T., Ziegler, J.: Facet Graphs: Complex Semantic Querying Made Easy. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 288–302. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Hogan, A., Umbrich, J., Harth, A., Cyganiak, R., Polleres, A., Decker, S.: An empirical survey of Linked Data conformance. Journal of Web Semantics 14, 14–44 (2012)CrossRefGoogle Scholar
  6. 6.
    Ferragina, P., Gulli, A.: A Personalized Search Engine Based on Web-Snippet Hierarchical Clustering. In: International World Wide Web Conference (WWW), pp. 801–810 (2005)Google Scholar
  7. 7.
  8. 8.
    Sah, M., Wade, V.: A Novel Concept-Based Search for the Web of Data Using UMBEL and a Fuzzy Retrieval Model. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 103–118. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Sah, M., Wade, V.: A Novel Concept-based Search for the Web of Data. In: I-SEMANTICS (2012)Google Scholar
  10. 10.
    Jansen, B.J., Booth, D.L., Spink, A.: Determining the User Intent of Web Search Engine Queries. In: International Conference on World Wide Web, pp. 1149–1150 (2007)Google Scholar
  11. 11.
    Ghorab, M.R., Zhou, D., O’Connor, A., Wade, V.: Personalised Information Retrieval: Survey and Classification. Journal of User Modeling and User-Adapted Interaction (to appear)Google Scholar
  12. 12.
    Sieg, Mobasher, B., Burke, R.: Web Search Personalization with Ontological User Profiles. In: International Conference on Information and Knowledge Management, CIKM (2007)Google Scholar
  13. 13.
    Fernandez, M., Lopez, V., Sabou, M., Uren, V., Vallet, D., Motta, E., Castells, P.: Using TREC for cross-comparison between classic IR and ontology-based search models at a Web scale. In: Workshop on Semantic Search (SemSearch 2009), WWW 2009 (2009)Google Scholar
  14. 14.
    Fernandez, M., Cantador, I., Lopez, V., Vallet, D., Castells, P., Motta, E.: Semantically enhanced Information Retrieval: An ontology-based approach. JWS 9(4), 434–452 (2011)CrossRefGoogle Scholar
  15. 15.
    Janowicz, K., Raubal, M., Kuhn, W.: The semantics of similarity in geographic information retrieval. Journal of Spatial Information Sciences (2), 29–57 (2011)Google Scholar
  16. 16.
    Tous, R., Delgado, J.: A vector space model for semantic similarity calculation and OWL ontology alignment. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 307–316. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Giunchiglia, F., Kharkevich, U., Zaihrayeu, I.: Concept Search. In: Aroyo, L., et al. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 429–444. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Mirizzi, R., Ragone, A., Di Noia, T., Di Sciascio, E.: Semantic wonder cloud: Exploratory search in DBpedia. In: Daniel, F., Facca, F.M. (eds.) ICWE 2010. LNCS, vol. 6385, pp. 138–149. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Tvarožek, M., Bieliková, M.: Factic: Personalized exploratory search in the semantic web. In: Benatallah, B., Casati, F., Kappel, G., Rossi, G. (eds.) ICWE 2010. LNCS, vol. 6189, pp. 527–530. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In: IEEE Symposium on Visual Languages (1996)Google Scholar
  21. 21.
  22. 22.
    D’Amato, C., Fanizzi, N., Esposito, F.: A semantic similarity measure for expressive description logics. Convegno Italiano di Logica Computazionale, CILC (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Melike Sah
    • 1
  • Vincent Wade
    • 1
  1. 1.Centre for Next Generation Localisation, KDEGTrinity College DublinDublinIreland

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