Information extraction from Wikipedia: moving down the long tail

ABSTRACT

Not only is Wikipedia a comprehensive source of quality information, it has several kinds of internal structure (e.g., relational summaries known as infoboxes), which enable self-supervised information extraction. While previous efforts at extraction from Wikipedia achieve high precision and recall on well-populated classes of articles, they fail in a larger number of cases, largely because incomplete articles and infrequent use of infoboxes lead to insufficient training data. This paper presents three novel techniques for increasing recall from Wikipedia's long tail of sparse classes: (1) shrinkage over an automatically-learned subsumption taxonomy, (2) a retraining technique for improving the training data, and (3) supplementing results by extracting from the broader Web. Our experiments compare design variations and show that, used in concert, these techniques increase recall by a factor of 1.76 to 8.71 while maintaining or increasing precision.

REFERENCES

• 1: Eugene Agichtein , Luis Gravano, Snowball: extracting relations from large plain-text collections, Proceedings of the fifth ACM conference on Digital libraries, p.85-94, June 02-07, 2000, San Antonio, Texas, United States [doi>10.1145/336597.336644]
• 2: Sören Auer , Jens Lehmann, What Have Innsbruck and Leipzig in Common? Extracting Semantics from Wiki Content, Proceedings of the 4th European conference on The Semantic Web: Research and Applications, June 03-07, 2007, Innsbruck, Austria [doi>10.1007/978-3-540-72667-8_36]
• 3: M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni. Open information extraction from the Web. In Proceedings of IJCAI07, 2007.
• 4: Avrim Blum , Tom Mitchell, Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory, p.92-100, July 24-26, 1998, Madison, Wisconsin, United States [doi>10.1145/279943.279962]
• 5: Eric Brill , Susan Dumais , Michele Banko, An analysis of the AskMSR question-answering system, Proceedings of the ACL-02 conference on Empirical methods in natural language processing, p.257-264, July 06, 2002 [doi>10.3115/1118693.1118726]
• 6: Philipp Cimiano , Günter Ladwig , Steffen Staab, Gimme' the context: context-driven automatic semantic annotation with C-PANKOW, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1060745.1060796]
• 7: W. Dakka and S. Cucerzan. Augmenting wikipedia with named entity tags. In Proceedings of IJCNLP 2008, 2008.
• 8: Stephen Dill , Nadav Eiron , David Gibson , Daniel Gruhl , R. Guha , Anant Jhingran , Tapas Kanungo , Sridhar Rajagopalan , Andrew Tomkins , John A. Tomlin , Jason Y. Zien, SemTag and seeker: bootstrapping the semantic web via automated semantic annotation, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775178]
• 9: Susan Dumais , Michele Banko , Eric Brill , Jimmy Lin , Andrew Ng, Web question answering: is more always better?, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564428]
• 10: Oren Etzioni , Michael Cafarella , Doug Downey , Ana-Maria Popescu , Tal Shaked , Stephen Soderland , Daniel S. Weld , Alexander Yates, Unsupervised named-entity extraction from the web: an experimental study, Artificial Intelligence, v.165 n.1, p.91-134, June 2005 [doi>10.1016/j.artint.2005.03.001]
• 11: J. Giles. Internet encyclopaedias go head to head. Nature, 438:900--901, December 2005.
• 12: Cody Kwok , Oren Etzioni , Daniel S. Weld, Scaling question answering to the web, ACM Transactions on Information Systems (TOIS), v.19 n.3, p.242-262, July 2001 [doi>10.1145/502115.502117]
• 13: John D. Lafferty , Andrew McCallum , Fernando C. N. Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Proceedings of the Eighteenth International Conference on Machine Learning, p.282-289, June 28-July 01, 2001
• 14: Dekang Lin , Patrick Pantel, DIRT @SBT@discovery of inference rules from text, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.323-328, August 26-29, 2001, San Francisco, California [doi>10.1145/502512.502559]
• 15: C. Matuszek, M. Witbrock, R. Kahlert, J. Cabral, D. Schneider, P. Shah, and D. Lenat. Searching for common sense: Populating Cyc from the Web. In Proceedings of AAAI05, 2005.
• 16: Andrew McCallum , Ronald Rosenfeld , Tom M. Mitchell , Andrew Y. Ng, Improving Text Classification by Shrinkage in a Hierarchy of Classes, Proceedings of the Fifteenth International Conference on Machine Learning, p.359-367, July 24-27, 1998
• 17: L. K. McDowell and M. Cafarella. Ontology-driven information extraction with ontosyphon. In Proceedings of ISWC06, 2006.
• 18: K. Nigam, J. Lafferty, and A. McCallum. Using maximum entropy for text classification. In Proceedings of Workshop on Machine Learning for Information Filtering, IJCAI99, 1999.
• 19: D. Opitz and R. Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, pages 169--198, 1999.
• 20: S. Patwardhan and E. Riloff. Effective information extraction with semantic affinity patterns and relevant regions. In Proceedings of EMNLP07, 2007.
• 21: Matthew Richardson , Pedro Domingos, Markov logic networks, Machine Learning, v.62 n.1-2, p.107-136, February 2006 [doi>10.1007/s10994-006-5833-1]
• 22: E. Riloff and J. Shepherd. A corpus-based approach for building semantic lexicons. In Proceedings of EMNLP97, 1997.
• 23: Rion Snow , Daniel Jurafsky , Andrew Y. Ng, Semantic taxonomy induction from heterogenous evidence, Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, p.801-808, July 17-18, 2006, Sydney, Australia [doi>10.3115/1220175.1220276]
• 24: C. Stein. Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. In Proceedings of the 3rd Berkeley Symposium on Mathematical Statistics and Probability), 2002.
• 25: Fabian M. Suchanek , Gjergji Kasneci , Gerhard Weikum, Yago: a core of semantic knowledge, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242667]
• 26: Fei Wu , Daniel S. Weld, Autonomously semantifying wikipedia, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal [doi>10.1145/1321440.1321449]
• 27: Fei Wu , Daniel S. Weld, Automatically refining the wikipedia infobox ontology, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367583]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS

General Terms:
Design, Management

Keywords:
Wikipedia, information extraction, semantic web