Skip to main content

Learning to Invoke Web Forms

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2888))

Abstract

Emerging Web standards promise a network of heterogeneous yet interoperable Web Services. Web Services would greatly simplify the development of many kinds of data integration systems, information agents and knowledge management applications. Unfortunately, this vision requires that services provide substantial quantities of explicit semantic metadata “glue”. As a step to automatically generating such metadata, we present an algorithm that learns to attach semantic labels to Web forms, and evaluate our approach on a large collection real Web data. The key idea is to cast Web form classification as Bayesian learning and inference over a generative model of the Web form design process.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Berger, A., Caruana, R., Cohn, D., Freitag, D., Mittal, V.: Bridging the lexical chasm: statistical approaches to answer-finding. In: Proc. Int. Conf. Research and Development in Information Retrieval, pp. 192–199 (2000)

    Google Scholar 

  2. Ciravegna, F.: Adaptive information extraction from text by rule induction and generalization. In: Proc. 17th Int. Conf. Artificial Intelligence, pp. 1251–1256 (2001)

    Google Scholar 

  3. Doan, A., Domingos, P., Halevy, A.: Reconciling schemas of disparate data sources: A machine-learning approach. In: Proc. SIGMOD Conference (2001)

    Google Scholar 

  4. Doorenbos, R., Etzioni, O., Weld, D.: A scalable comparison-shopping agent for the World-Wide Web. In: Proc. Int. Conf. Autonomous Agents, pp. 39–48 (1997)

    Google Scholar 

  5. Heß, A., Kushmerick, N.: Learning to attach semantic metadata to Web Services. In: Proc. Int. Semantic Web Conf. (2003)

    Google Scholar 

  6. Hsu, C., Dung, M.: Generating finite-state transducers for semistructured data extraction from the web. J. Information Systems 23(8), 521–538 (1998)

    Article  Google Scholar 

  7. Ives, Z., Levy, A., Weld, D., Florescu, D., Friedman, M.: Adaptive query processing for Internet applications. IEEE Data Engineering Bulletin 23(2) (2000)

    Google Scholar 

  8. Kaljuvee, O., Buyukkokten, O., Garcia-Molina, H., Paepcke, A.: Efficient Web form entry on PDAs. In: Proc. 10th World Wide Web Conference, vol. 5, pp. 663–672 (2001)

    Google Scholar 

  9. Kushmerick, N.: Wrapper induction: Efficiency and expressiveness. Artificial Intelligence 118(1-2), 15–68 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  10. Lewis, D.: Evaluating text categorization. In: Proc. Speech and Natural Language Workshop, pp. 312–318 (1991)

    Google Scholar 

  11. Liddle, S., Embley, D., Scott, D., Yau, S.: Extracting data behing Web forms. In: Proc. Int. Conf. Very Large Databases (2002)

    Google Scholar 

  12. McCallum, A., Rosenfeld, R., Mitchell, T., Ng, A.: Improving text classification by shrinkage in a hierarchy of classes. In: Proc 15th Int. Conf. Machine Learning, pp. 359–367 (1998)

    Google Scholar 

  13. Muslea, I., Minton, S., Knoblock, C.: A Hierachical Approach to Wrapper Induction. In: Proc. 3rd Int. Conf. Autonomous Agents, pp. 190–197 (1999)

    Google Scholar 

  14. Nahm, U., Mooney, R.: A mutually beneficial integration of data mining and information extraction. In: Proc. 17th Nat. Conf. Artificial Intelligence, pp. 627– 632 (2000)

    Google Scholar 

  15. Pearl, J.: Probablistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  16. Perkowitz, M., Etzioni, O.: Category translation: Learning to understand information on the Internet. In: Proc. 14th Int. Conf. Artificial Intelligence, pp. 930–938 (1995)

    Google Scholar 

  17. Popescul, A., Ungar, L., Pennock, D., Lawrence, S.: Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: Proc. 17th Conf. Uncertainty in Artificial Intelligence, pp. 437–444 (2001)

    Google Scholar 

  18. Raghavan, S., Garcia-Molina, H.: Crawling the hidden Web. In: Proc. 27th Int. Conf. Very Large Databases, pp. 129–138 (2001)

    Google Scholar 

  19. Yi, J., Sundaresan, N.: A classifier for semi-structured documents. In: Proc. Conf. Knowledge Discovery in Data, pp. 190–197 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kushmerick, N. (2003). Learning to Invoke Web Forms. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds) On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. OTM 2003. Lecture Notes in Computer Science, vol 2888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39964-3_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-39964-3_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20498-5

  • Online ISBN: 978-3-540-39964-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics