Corroborate and learn facts from the web

ABSTRACT

The web contains lots of interesting factual information about entities, such as celebrities, movies or products. This paper describes a robust bootstrapping approach to corroborate facts and learn more facts simultaneously. This approach starts with retrieving relevant pages from a crawl repository for each entity in the seed set. In each learning cycle, known facts of an entity are corroborated first in a relevant page to find fact mentions. When fact mentions are found, they are taken as examples for learning new facts from the page via HTML pattern discovery. Extracted new facts are added to the known fact set for the next learning cycle. The bootstrapping process continues until no new facts can be learned. This approach is language-independent. It demonstrated good performance in experiment on country facts. Results of a large scale experiment will also be shown with initial facts imported from wikipedia.

References

1. R. G. Bing Liu and Y. Zhao. Mining data records in web pages. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2003), pages 601--606, Washington, D.C, 2003.
2. S. Brin. Extracting patterns and relations from the world wide web. In WebDB '98: Selected papers from the International Workshop on The World Wide Web and Databases, pages 172--183, London, UK, 1999. Springer-Verlag.
3. S.-C. L. Chia-Hui Chang. Iepad: Information extraction based on pattern discovery. In Proceedings of The tenth International World Wide Web Conference (WWW).
4. W. Cohen, M. Hurst, and L. Jensen. A flexible learning system for wrapping tables and lists in html documents. In Proceedings of The Eleventh International World Wide Web Conference (WWW), 2002.
5. J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters. In Proceedings of the Sixth Symposium on Operating System Design and Implementation (OSDI'04), San Francisco, CA, 2004.
6. O. Etzioni, M. Cafarella, D. Downey, S. Kok, A. Popescu, T. Shaked, S. Soderland, D. Weld, and A. Yates. Web-scale information extraction in knowitall. 2004.
7. R. Feldman, B. Rosenfeld, S. Soderland, and O. Etzioni. Self-supervised relation extraction from the web. In ISMIS, pages 755--764, 2006.
8. S. Harabagiu, M. Pasca, and S. Maiorano. Experiments with open-domain textual question answering. In Proceedings of the 18th conference on Computational linguistics, pages 292--298, Morristown, NJ, USA, 2000. Association for Computational Linguistics.
9. I. Muslea, S. Minton, and C. A. Knoblock. Hierarchical wrapper induction for semistructured information sources. Autonomous Agents and Multi-Agent Systems, 4(1/2):93-114, 2001.
10. D. W. N. Kushmerick and R. Doorenbos. Wrapper induction for information extraction. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages 729--737, San Francisco, CA, 1997.
11. D. Ravichandran and E. Hovy. Learning surface text patterns for a question answering system. In ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pages 41--47, Morristown, NJ, USA, 2001. Association for Computational Linguistics.

Index Terms

• Computing methodologies

  • Machine learning

    • Learning settings

• Information systems

  • Information retrieval