Abstract
Entity relation extraction is mainly focused on researching extraction approaches and improving precision of the extraction results. Although many efforts have been made on this field, there still exist some problems. In order to improve the performance of extracting entity relation, we propose a tuple refinement method based on relationship keyword extension. Firstly, we utilize the diversity of relationships to extend relationship keywords, and then, use the redundancy of network information to extract the second entity based on the principle of proximity and the predefined entity type. Under open web environment, we take four relationships in the experiments and adopt bootstrapping algorithm to acquire the initial tuple set. Three tuple refinement methods are compared: refinement method with threshold set, refinement method with relation extension and refinement method without relation extension. The average F-scores of the experimental results show the proposed method can effectively improve the performance of entity relation extraction.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Eugene, A., Luis, C.: Snowball: Extracting relations from large plain-text collections. In: 5th ACM International Conference on Digital Libraries, New York, pp. 85–94 (2000)
Brin, S.: Extracting Patterns and Relations from the World Wide Web. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 172–183. Springer, Heidelberg (1999)
Eugene, A.: Confidence Estimation Methods for Partially Supervised Relation Extraction. In: Proc. of SIAM Intl. Conf. on Data Mining, SDM 2006 (2006)
Frank, R., Hannes, K., Gerhard, P.: Semantic Relation Extraction With Kernels Over Typed Dependency Trees. In: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, pp. 773–782 (2010)
Yuan, F.K., Chen-Chuan, C.: Searching Patterns for Relation Extraction over the Web: Rediscovering the Pattern-Relation Duality. In: The 4th ACM International Conference on Web Search and Data Mining, New York, pp. 825–834 (2011)
Jeoghee, Y., Neel, S.: Mining the web for acronyms using the duality of patterns and relations. In: The 2nd International Workshop on Web Information and Data Management, New York, pp. 48–52 (1999)
Danushka, B., Yutaka, M., Mitsuru, L.: Relational Duality: Unsupervised Extraction of Semantic Relations between Entities on the Web. In: The 2nd International Conference on World Wide Web, New York, pp. 151–160 (2010)
Li, W.G., Liu, T., Li, S.: Automated Entity Relation Tuple Extraction Using Web Mining. Acta Electronica Sinica (11), 2111–2116 (2007)
Yao, C.L., Di, N.: A relation extraction method based on large-scale characters web social. Pattern Recognition and Artificial Intelligence (6), 740–744 (2007)
Grishman, R.: Information Extraction: Techniques and Challenges. In: Pazienza, M.T. (ed.) SCIE 1997. LNCS, vol. 1299, pp. 10–27. Springer, Heidelberg (1997)
Aron, C., Andrew, M.: Confidence estimation for information extraction. In: 2004 HLT-NAACL, pp. 109–112 (2004)
Shubin, Z., Ralph, G.: Extracting relations with integrated information using kernel methods. In: 43rd Annual Meeting on ACL, pp. 419–426 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, X., Yang, J., Chen, C. (2012). Tuple Refinement Method Based on Relationship Keyword Extension. In: Wang, F.L., Lei, J., Gong, Z., Luo, X. (eds) Web Information Systems and Mining. WISM 2012. Lecture Notes in Computer Science, vol 7529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33469-6_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-33469-6_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33468-9
Online ISBN: 978-3-642-33469-6
eBook Packages: Computer ScienceComputer Science (R0)