Data Extraction from Semi-structured Web Pages by Clustering

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 identified 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