Data Extraction from Semi-structured Web Pages by Clustering

Authors:
Le Phong Bao Vuong;Xiaoying Gao;Mengjie Zhang
Affiliations:
Victoria University of Wellington, New Zealand;Victoria University of Wellington, New Zealand;Victoria University of Wellington, New Zealand
Venue:
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Year:
2006

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Abstract

This paper introduces an approach to the use of clustering for data extraction from semi-structured Web pages. A variant Hierarchical Agglomerative Clustering (HAC) algorithm K-neighbours-HAC is developed which uses the similarities of the data format (HTML tags) and the data content (text string values) to group similar text tokens into clusters. Using these clusters, similar text tokens are identi- fied as data fields and extracted as target information. The approach is examined and compared with a number of existing information extraction systems on two different sets of web pages and the results suggest that the new approach is effective for web information extraction and that it outperforms all of the existing approaches on these web sites.