A Practical Agent-Based Method to Extract Semantic Information from the Web

  • J. L. Arjona
  • R. Corchuelo
  • A. Ruiz
  • M. Toro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2348)


The semantic Web will bring meaning to the Internet, making it possible for web agents to understand the information it contains. However, current trends seem to suggest that it is not likely to be adopted in the forthcoming years. In this sense, meaningful information extraction from the web becomes a handicap for web agents. In this article, we present a framework for automatic extraction of semantically-meaningful information from the current web. Separating the extraction process from the business logic of an agent enhances modularity, adaptability, and maintainability. Our approach is novel in that it combines different technologies to extract information, surf the web and automatically adapt to some changes.


Information Channel Inductive Logic Programming Business Logic Extraction Rule Defense Advance Research Project Agency 
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.


  1. 1.
    DARPA (Defense Advanced Research Projects Agency). The darpa agent mark up language (daml)., 2000.
  2. 2.
    W. W. Cohen and L. S. Jensen. A structured wrapper induction system for extracting information from semi-structured documents. In Workshop on Adaptive Text Extraction and Mining (IJCAI-2001), 2001.Google Scholar
  3. 3.
    O. Corcho and A. Gómez-Pérez. A road map on ontology specification languages. In Workshop on Applications of Ontologies and Problem solving methods. 14th European Conference on Artificial Intelligence (ECAI’00), 2000.Google Scholar
  4. 4.
    S. Cranefield and M. Purvis. Generating ontology-specific content languages. In Proceedings of Ontologies in Agent Systems Workshop (Agents 2001), pages 29–35, 2000.Google Scholar
  5. 5.
    H. García-Molina, J. Hammer, K. Ireland, Y. Papakonstantinou, J. Ullman, and J. Widom. Integrating and accessing heterogeneous information sources in TSIM-MIS. In The AAAI Symposium on Information Gathering, pages 61–64, March 1995.Google Scholar
  6. 6.
    C. A. Knoblock. Accurately and reliably extracting data from the web: A machine learning approach. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2000.Google Scholar
  7. 7.
    N. Kushmerick. Wrapper induction: Efficiency and expressiveness. Artificial Intelligence, 118(2000):15–68, 1999.MathSciNetGoogle Scholar
  8. 8.
    G. Mecca, P. Merialdo, and P. Atzeni. ARANEUS in the era of XML. Data Engineering Bullettin, Special Issue on XML, September 1999.Google Scholar
  9. 9.
    I. Muslea, S. Minton, and C. Knoblock. Wrapper induction for semistructured, web-based information sources. In Proceedings of the Conference on Automated Learning and Discovery (CONALD), 1998.Google Scholar
  10. 10.
    S. Soderland. Learning information extraction rules for semi-structured and free text. Machine Learning, pages 1–44, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • J. L. Arjona
    • 1
  • R. Corchuelo
    • 1
  • A. Ruiz
    • 1
  • M. Toro
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosEscuela Técnica Superior de Ingeniería Informática de la Universidad de SevillaSevillaSpain

Personalised recommendations