Bridging the Syntactic and the Semantic Web Search

  • Georgios Kouzas
  • Ioannis Anagnostopoulos
  • Ilias Maglogiannis
  • Christos Anagnostopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


This paper proposes an information system, which aims to bridge the semantic gap in web search. The system uses multiple domain ontological structures expanding the user’s query with semantically related concepts, enhancing in parallel the quality of retrieval to a large extend. Query analyzers broaden the user’s information needs from classical term-based to conceptually representations, using knowledge from relevant ontologies and theirs’ properties. Besides the use of semantics, the system employs machine learning techniques from the field of swarm intelligence through the Ant Colony algorithm, where ants are considered as web agents capable of collecting and processing relevant information. Furthermore, the effectiveness of the approach is verified experimentally, by observing that the retrieval precision for the enhanced queries is in higher levels, in comparison with the results derived from the classical term-based retrieval procedure.


Search Engine Query Term Initial Pheromone Probabilistic Neural Network Classifier Probabilistic Travel Salesman Problem 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anagnostopoulos, I., Anagnostopoulos, C., Kouzas, G., Vergados, D.: A Generalised Regression algorithm for web page categorisation. Neural Computing & Applications journal 13(3), 229–236 (2004)CrossRefGoogle Scholar
  2. 2.
    Anagnostopoulos, I., Anagnostopoulos, C., Loumos, V., Kayafas, E.: Classifying Web Pages employing a Probabilistic Neural Network Classifier. IEE Proceedings – Software 151(03), 139–150 (2004)CrossRefGoogle Scholar
  3. 3.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: WWW7: Proceedings of the seventh international conference on World Wide Web 7. Elsevier Science Publishers B. V, Amsterdam (1998)Google Scholar
  4. 4.
    Landauer, T.K., Foltz, P.W., Laham, D.: Introduction to Latent Semantic Analysis. Discourse Processes 25, 259–284 (1998)CrossRefGoogle Scholar
  5. 5.
    Brickley, D., Guha, R.V.: Rdf schema,
  6. 6.
    Anagnostopoulos, I., Psoroulas, I., Loumos, V., Kayafas, E.: Implementing a customized meta-search interface for user query personalization. In: 24th International Conference on In-formation Technology Interfaces, ITI 2002, June 24-27, pp. 79–84. Cavtat/ Dubrovnik (2002)Google Scholar
  7. 7.
    Dorigo, M., Maniezzo, V.: The ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics 26(1), 1–13 (1996)Google Scholar
  8. 8.
    Craswell, N., Hawking, D., Thistlewaite, P.: Merging Results from Isolated Search Engines. In: 10th Australasian Database Conference, Auckland, New Zealand, January 1999. Springer, Singapore (1999)Google Scholar
  9. 9.
    Yuwono, B., Lee, D.L.: Server ranking for distributed text retrieval systems on the internet. In: Topor, R., Tanaka, K. (eds.) DASFAA 1997, Melbourne, pp. 41–49. World Scientific, Singapore (1997)Google Scholar
  10. 10.
    Ding, L., Finin, T., Joshi, A., Pan, R., Cost, R.S., Peng, Y., Reddivari, P., Doshi, V.C., Sachs, J.: Swoogle: A search and metadata engine for the semantic web. In: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management, Washington, DC (November 2004)Google Scholar
  11. 11.
    Jena Semantic Web Framework,
  12. 12.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)MATHGoogle Scholar
  13. 13.
    Dorigo, M., Caro, G.D.: The Ant Colony Optimization Meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill, London (1999)Google Scholar
  14. 14.
    Dorigo, M., Caro, G.D.: Ant Algorithms Optimization. Artificial Life 5(3), 137–172 (1999)CrossRefGoogle Scholar
  15. 15.
    Chen, S., Smith, S.: Commonality and genetic algorithms. Technical Report CMURITR- 96-27, The Robotic Institute, Carnegie Mellon University, Pittsburgh, PA, USA (1996)Google Scholar
  16. 16.
    Bianchi, L., Gambardella, L.M., Dorigo, M.: An ant colony optimization approach to the probabilistic traveling salesman problem. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, p. 883. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Georgios Kouzas
    • 1
  • Ioannis Anagnostopoulos
    • 2
  • Ilias Maglogiannis
    • 2
  • Christos Anagnostopoulos
    • 3
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece
  2. 2.Department of Information and Communication Systems EngineeringUniversity of the AegeanKarlovassi, SamosGreece
  3. 3.Department of Cultural Technology and CommunicationUniversity of the AegeanMytiline, LesvosGreece

Personalised recommendations