Abstract
For the purposes of this chapter, an information agent can be described as a distributed system that receives a goal through its user interface, gathers information relevant to this goal from a variety of sources, processes this content as appropriate, and delivers the results to the users. We focus on the second stage in this generic architecture. We survey a variety of information extraction techniques that enable information agents to automatically gather information from heterogeneous sources.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
F. Bergadano and D. Gunetti. Inductive Logic Programming. MIT Press, 1996.
G. Beuster, B. Thomas, and C. Wolff. MIA-A Ubiquitous Multi-Agent Web Information System. In Proceedings of International ICSC Symposium on Multi-Agents and MobileAgents in Virtual Organizations and E-Commerce (MAMA’2000), December 2000.
D. Bikel, S. Miller, R. Schwartz, and R. Weischedel. Nymble: A high-performance learning name-finder. In Proc. Conf. on Applied Natural Language Processing, 1997.
S. Brin. Extracting patterns and relations from the World Wide Web. In Proc. SIGMOD Workshop on Databases and the Web, 1998.
M. E. Califf. Relational Learning Techniques for Natural Language Information Extraction. PhD thesis, University of Texas at Austin, August 1998.
F. Ciravegna. Learning to Tag for Information Extraction from Text. In Workshop Machine Learning for Information Extraction, European Conference on Artifical Intelligence ECCAI, August 2000. Berlin, Germany.
P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3:261–283, 1989.
W. Cohen and L. Jensen. A structured wrapper induction system for extracting information from semi-structured documents.
V. Crescenzi, G. Mecca, and P. Merialdo. Roadrunner: Towards automatic data extraction from large web sites. In The VLDB Journal, pages 109–118, 2001.
D. Freitag. Machine Learning for Information Extraction in Informal Domains. PhD thesis, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, November 1998.
D. Freitag and N. Kushmerick. Boosted Wrapper Induction. In Proceedings of the Seventh National Conference on Artificial, pages 577–583, July 30–August 3 2000. Austin, Texas.
D. Freitag and A. McCallum. Information Extraction withHMMstructures learned by stochastic optimization. In Proceedings of the Seventh National Conference on Artificial, July 30–August 3 2000. Austin, Texas.
G. Grieser, K. P. Jantke, S. Lange, and B. Thomas. A Unifying Approach to HTML Wrapper Representation and Learning. In Proceedings of the Third International Conference on Discovery Science, December 2000. Kyoto, Japan.
C. Hsu and M. Dung. Generating finite-state transducers for semistructured data extraction from the web. J. Information Systems, 23(8):521–538, 1998.
L. Jensen and W. Cohen. Grouping extracted fields. In Proc. IJCAI-01Workshop on Adaptive Text Extraction and Mining, 2001.
M. Junker, M. Sintek, and M. Rinck. Learning for Text Categorization and Information Extraction with ILP. In Proc. Workshop on Learning Language in Logic, June 1999. Bled, Slovenia.
N. Kushmerick. Wrapper Induction for Information Extraction. PhD thesis, University of Washington, 1997.
N. Kushmerick. Regression testing for wrapper maintenance. In Proc. National Conference on Artificial Intelligence, pages 74–79, 1999.
N. Kushmerick. Wrapper induction: Efficiency and expressiveness. Artificial Intelligence, 118(1–2):15–68, 2000.
N. Kushmerick. Wrapper verification. World Wide Web Journal, 3(2):79–94, 2000.
N. Kushmerick, D. S. Weld, and R. Doorenbos. Wrapper Induction for Information Extraction. In M. E. Pollack, editor, Fifteenth International Joint Conference on Artificial Intelligence, volume 1, pages 729–735, August 1997. Japan.
T. Leek. Information extraction using hidden Markov models. Master’s thesis, University of California, San Diego, 1997.
K. Lerman and S. Minton. Learning the common structure of data. In Proc. National Conference on Artificial Intelligence, 2000.
T. M. Mitchell. Machine Learning. McGraw-Hill, 1997.
S. Muggleton and L. D. Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19(20):629–679, 1994.
I. Muslea. Extraction patterns for information extraction tasks: A survey. In Proc. AAAI-99 Workshop on Machine Learning for Information Extraction, 1999.
I. Muslea, S. Minton, and C. Knoblock. A hierarchical approach to wrapper induction. In Proc. Third International Conference on Autonomous Agents, pages 190–197, 1999.
I. Muslea, S. Minton, and C. Knoblock. Selective sampling with redundant views. In Proc. National Conference on Artificial Intelligence, 2000.
J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239–266, 1990.
E. M. Riloff. Information Extraction as a Basis for Portable Text Classification Systems. PhD thesis, University of Massachusetts Amherst, 1994.
K. Seymore, A. McCallum, and R. Rosenfeld. Learning hidden Markov model structure for information extraction. In Proc. AAAI-99 Workshop on Machine Learning for Information Extraction, 1999.
S. Soderland. Learning information extraction rules for semi-structured and free text. Machine Learning, 34(1–3):233–272, 1999.
S. G. Soderland. Learning Text Analysis Rules for Domain-Specific Natural Language Processing.PhD thesis, University of Massachusetts Amherst, 1997.
B. Thomas. Anti-Unification Based Learning of T-Wrappers for Information Extraction. In Proc. AAAI-99 Workshop on Machine Learning for Information Extraction, 1999.
B. Thomas. Token-Templates and Logic Programs for Intelligent Web Search. Intelligent Information Systems, 14(2/3):241–261, March-June 2000. Special Issue: Methodologies for Intelligent Information Systems.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kushmerick, N., Thomas, B. (2003). Adaptive Information Extraction: Core Technologies for Information Agents. In: Klusch, M., Bergamaschi, S., Edwards, P., Petta, P. (eds) Intelligent Information Agents. Lecture Notes in Computer Science(), vol 2586. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36561-3_4
Download citation
DOI: https://doi.org/10.1007/3-540-36561-3_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00759-3
Online ISBN: 978-3-540-36561-7
eBook Packages: Springer Book Archive