  Many websites with an underlying database containing structured data provide
the richest and most dense source of information relevant for topical data
integration. The real data integration requires sustainable and reliable
pattern discovery to enable accurate content retrieval and to recognize pattern
changes from time to time; yet, extracting the structured data from web
documents is still lacking from its accuracy. This paper proposes the bar-tree
representation to describe the whole pattern of web pages in an efficient way
based on the reverse algorithm. While previous algorithms always trace the
pattern and extract the region of interest from \textit{top root}, the reverse
algorithm recognizes the pattern from the region of interest to both top and
bottom roots simultaneously. The attributes are then extracted and labeled
reversely from the region of interest of targeted contents. Since using
conventional representations for the algorithm should require more
computational power, the bar-tree method is developed to represent the
generated patterns using bar graphs characterized by the depths and widths from
the document roots. We show that this representation is suitable for extracting
the data from the semi-structured web sources, and for detecting the template
changes of targeted pages. The experimental results show perfect recognition
rate for template changes in several web targets.
