Skip to main content

Semantic Web Technologies and Artificial Neural Networks for Intelligent Web Knowledge Source Discovery

  • Chapter
Emergent Web Intelligence: Advanced Semantic Technologies

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

This chapter is focused on presenting new and recent techniques, such as the combination of agent-based technologies and Artificial Neural Network (ANN) models that can be used for intelligent web knowledge source discovery in the new and emergent Semantic Web.

The purpose of the Semantic Web is to introduce semantic content in the huge amount of unstructured or semi-structured information sources available on the web by using ontologies. An ontology provides a vocabulary about concepts and their relationships within a domain, the activities taking place in that domain, and the theories and elementary principles governing that domain. The lack of an integrated view of all sources and the existence of heterogeneous domain ontologies, drives new challenges in the discovery of knowledge sources relevant to a user request. New efficient techniques and approaches for developing web intelligence are presented in this chapter, to help users avoid irrelevant web search results and wrong decision making.

In summary, the contributions of this chapter are twofold:

  1. 1.

    The benefits of combining Artificial Neural Networks with Semantic Web Technologies are discussed.

  2. 2.

    An Artificial Neural Network-based intelligent agent with capabilities for discovering distributed knowledge sources is presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://cordis.europa.eu/fp7.

  2. 2.

    http://protege.cim3.net/file/pub/ontologies/ka/ka.owl.

  3. 3.

    http://ontoware.org/projects/swrc/.

References

  1. Baeza-Yates, R.: Web mining. In: Proc. LA-WEB Congress, p. 2 (2005)

    Google Scholar 

  2. Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 5(1), 29–37 (2001)

    Google Scholar 

  3. Breitman, K., Casanova, M.A., Truszkowski, W.: Semantic Web: Concepts, Technologies and Applications. Springer, London (2007)

    MATH  Google Scholar 

  4. Castano, S., Ferrara, A., Montanelli, S.: Dynamic knowledge discovery in open, distributed and multi-ontology systems: techniques and applications. In: Web Semantics and Ontology. Idea Group Inc, London (2006)

    Google Scholar 

  5. Chortaras, A., Stamou, G.B., Stafylopatis, A.: Learning ontology alignments using recursive neural networks. In: Proc. Int. Conf. on Neural Networks (ICANN), Poland. Lecture Notes in Computer Science, vol. 3697, pp. 811–816. Springer, Berlin (2005)

    Google Scholar 

  6. Cybenko, G.: Neural networks in computational science and engineering. IEEE Computational Science and Engineering 3(1), 36–42 (1996)

    Article  Google Scholar 

  7. Curino, C., Orsi, G., Tanca, L.: X-SOM: Ontology mapping and inconsistency resolution. In: 4th European Semantic Web Conference (ESWC’07), 3–7, June 2007

    Google Scholar 

  8. Davies, J., Studer, R., Warren, P.: Semantic Web Technologies: Trends and Research in Ontology-Based Systems. Wiley, London (2007)

    Google Scholar 

  9. Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Ontology matching: A machine learning approach. In: Handbook on Ontologies in Information Systems, pp. 385–403. Springer, New York (2004)

    Google Scholar 

  10. Ehrig, M., Sure, Y.: Ontology mapping—an integrated approach. In: Proc. 1st European Semantic Web Symposium (ESWS 2004), Greece. Lecture Notes in Computer Science, vol. 3053, pp. 76–91. Springer, Berlin (2004)

    Google Scholar 

  11. Euzenat, J., Barrasa, J., Bouquet, P., Bo, J.D., et al.: State of the art on ontology alignment. D2.2.3, Technical Report IST-2004-507482, KnowledgeWeb, 2004

    Google Scholar 

  12. Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, London (2007)

    MATH  Google Scholar 

  13. Gómez-Pérez, A., Fernández-López, M., Corcho, O.: Ontological Engineering—with Examples from the Areas of Knowledge Management, e-Commerce and the Semantic Web. Springer, London (2004)

    Google Scholar 

  14. Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, New York (1999)

    MATH  Google Scholar 

  15. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359–366 (1989)

    Article  Google Scholar 

  16. Hendler, J.: Agents and the Semantic Web. IEEE Intelligent Systems 16(2), 30–37 (2001)

    Article  Google Scholar 

  17. Huang, J., Dang, J., Vidal, J., Huhns, M.: Ontology matching using an artificial neural network to learn weights. In: Proc. IJCAI Workshop on Semantic Web for Collaborative Knowledge Acquisition (SWeCKa-07), India (2007)

    Google Scholar 

  18. Lam, T., Lee, R.: iJADE FreeWalker—an intelligent ontology agent-based tourist guiding system. Studies in Computational Intelligence 72, 103–125 (2007)

    Article  Google Scholar 

  19. Li, W., Clifton, C.: SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks. Data and Knowledge Engineering 33(1), 49–84 (2000)

    Article  MATH  Google Scholar 

  20. López, V., Motta, E., Uren, V.: PowerAqua: fishing the semantic web. In: Proc. 3rd European Semantic Web Conference, Montenegro. Lecture Notes in Computer Science, vol. 4011, pp. 393–410. Springer, Berlin (2006)

    Google Scholar 

  21. Maes, P.: Intelligent software. Scientific American 273(3), 84–86 (1995)

    MathSciNet  Google Scholar 

  22. Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics 11, 431–441 (1963)

    Article  MathSciNet  MATH  Google Scholar 

  23. Nejdl, W., Wolf, B., Qu, C., Decker, S., Sintek, M., Naeve, A., Nilsson, M., Palmér, M., Risch, T.: EDUTELLA, A P2P networking infrastructure based on RDF. In: Proc. 11th World Wide Web Conference (WWW2002), USA, pp. 604–615 (2002)

    Google Scholar 

  24. Peis, E., Herrera-Viedma, E., Montero, Y.H., Herrera, J.C.: Ontologías, metadatos y agentes: Recuperación semántica de la información. In: Proc. II Jornadas de Tratamiento y Recuperación de la Información, España, pp. 157–165 (2003)

    Google Scholar 

  25. Pinkus, A.: Approximation theory of the MLP model in neural networks. Acta Numerica 1, 143–195 (1999)

    Article  MathSciNet  Google Scholar 

  26. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  27. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, New York (2002)

    Google Scholar 

  28. Stegmayer, G., Caliusco, M.L., Chiotti, O., Galli, M.R.: ANN-agent for distributed knowledge source discovery. In: On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops. Lecture Notes in Computer Science, vol. 4805, pp. 467–476. Springer, Berlin (2007)

    Chapter  Google Scholar 

  29. Werbos, P.: The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting. Wiley, New York (1994)

    Google Scholar 

  30. Wermter, S.: Neural network agents for learning semantic text classification. Information Retrieval 3(2), 87–103 (2000)

    Article  Google Scholar 

  31. Wooldridge, M.: An Introduction to Multiagent Systems. Wiley, New York (2002)

    Google Scholar 

  32. Wray, J., Green, G.: Neural networks, approximation theory and precision computation. Neural Networks 8(1), 31–37 (1995)

    Article  Google Scholar 

  33. Zhu, X., Huang, S., Yu, Y.: Recognizing the relations between Web pages using artificial neural network. In: Proc. ACM Symposium on Applied Computing, USA, pp. 1217–1221 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. L. Caliusco .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag London

About this chapter

Cite this chapter

Caliusco, M.L., Stegmayer, G. (2010). Semantic Web Technologies and Artificial Neural Networks for Intelligent Web Knowledge Source Discovery. In: Badr, Y., Chbeir, R., Abraham, A., Hassanien, AE. (eds) Emergent Web Intelligence: Advanced Semantic Technologies. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-077-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-84996-077-9_2

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-076-2

  • Online ISBN: 978-1-84996-077-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics