Extracting Loosely Structured Data Records Through Mining Strict Patterns

Authors:
Yipu Wu;Jing Chen;Qing Li
Affiliations:
Department of Computer Science, City University of Hong Kong, 83. Tat Chee Avenne, Kowloon, Hong Kong. yipuwu@cityu.edu.hk;Department of Computer Science, City University of Hong Kong, 83. Tat Chee Avenne, Kowloon, Hong Kong. jerryjin@cityu.edu.hk;Department of Computer Science, City University of Hong Kong, 83. Tat Chee Avenne, Kowloon, Hong Kong. itqli@cityu.edu.hk
Venue:
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Year:
2008

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Abstract

Extracting loosely structured data records (DRs) has wide applications in many domains, such as forum pattern recognition, blog data analysis, and books and news review analysis. Currently existing methods work well for strongly structured DRs only. In this paper, we address the problem of extracting loosely structured DRs through mining strict patterns. In our method, we utilize both content feature and tag tree feature to recognize the loosely structured DRs, and propose a new approach to extract the DRs automatically. Through experimental study we demonstrate that this method is both effective and robust in practice.