Abstract
In this paper we describe a methodology for harvesting information from large distributed repositories (e.g. large Web sites) with minimum user intervention. The methodology is based on a combination of information extraction, information integration and machine learning techniques. Learning is seeded by extracting information from structured sources (e.g. databases and digital libraries) or a user-defined lexicon. Retrieved information is then used to partially annotate documents. Annotated documents are used to bootstrap learning for simple Information Extraction (IE) methodologies, which in turn will produce more annotation to annotate more documents that will be used to train more complex IE engines and so on. In this paper we describe the methodology and its implementation in the Armadillo system, compare it with the current state of the art, and describe the details of an implemented application. Finally we draw some conclusions and highlight some challenges and future work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Brin, S.: Extracting patterns and relations from the world wide web. In: WebDB Workshop at 6th International Conference on Extending Database Technology, EDBT 1998 (1998)
Ciravegna, F.: Adaptive information extraction from text by rule induction and generalisation. In: Proceedings of 17th International Joint Conference on Artificial Intelligence (IJCAI), Seattle (2001)
Ciravegna, F.: Designing adaptive information extraction for the SemanticWeb in Amilcare. In: Handschuh, S., Staab, S. (eds.) Annotation for the Semantic Web, Frontiers in Artificial Intelligence and Applications, IOS Press, Amsterdam (2003)
Dill, S., Eiron, N., Gibson, D., Gruhl, D., Guha, R., Jhingran, A., Kanungo, T., Rajagopalan, S., Tomkins, A., Tomlin, J.A., Zien, J.Y.: SemTag and Seeker: Bootstrapping the semantic web via automated semantic annotation. In: Proceedings of the World Wide Web Conference 2003 (2003)
Dumais, S., Banko, M., Brill, E., Lin, J., Ng, A.: Web question answering: Is more always better? In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002), Tampere, Finland (2002)
Dzbor, M., Domingue, J.B., Motta, E.: Magpie - towards a semantic web browser. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 690–705. Springer, Heidelberg (2003)
Goble, C., Bechhofer, S., Carr, L., De Roure, D., Hall, W.: Conceptual Open Hypermedia = The Semantic Web? In: The Second International Workshop on the Semantic Web, Hong Kong, May 2001, pp. 44–50 (2001)
Handschuh, S., Staab, S., Ciravegna, F.: S-CREAM - Semi-automatic CREAtion of Metadata. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, p. 358. Springer, Heidelberg (2002)
Kogut, P., Holmes, W.: Applying information extraction to generate daml annotations from web pages. In: Proceedings of the K-CAP 2001 Workshop Knowledge Markup & Semantic Annotation, Victoria B.C., Canada (2001)
Kushmerick, N., Weld, D., Doorenbos, R.: Wrapper induction for information extraction. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (1997)
Leonard, T., Glaser, H.: Large scale acquisition and maintenance from the web without source access. In: Handschuh, S., Dieng-Kuntz, R., Staab, S. (eds.) Proceedings Workshop 4, Knowledge Markup and Semantic Annotation, K-CAP 2001 (2001)
Mitchell, T.: Extracting targeted data from the web. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, San Francisco, California (2001)
Perkowitz, M., Etzioni, O.: Category translation: Learning to understand information on the internet. In: International Joint Conference on Artificial Intelligence, IJCAI 1995, Montreal, Canada, pp. 930–938 (1995)
Vargas-Vera, M., Motta, E., Domingue, J., Lanzoni, M., Stutt, A., Ciravegna, F.: MnM: Ontology driven semi-automatic or automatic support for semantic markup. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, p. 379. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ciravegna, F., Chapman, S., Dingli, A., Wilks, Y. (2004). Learning to Harvest Information for the Semantic Web. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds) The Semantic Web: Research and Applications. ESWS 2004. Lecture Notes in Computer Science, vol 3053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25956-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-25956-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21999-6
Online ISBN: 978-3-540-25956-5
eBook Packages: Springer Book Archive