Web wrapper generation using tree alignment and transfer learning

This paper studies the web wrapper generation for web pages of forum, blog and news web sites. While more and more web pages are dynamically generated using a common template populated with data from databases. This paper proposes a novel method that uses tree alignment and transfer learning method to generate the wrapper from this kind of web pages. We present a new tree alignment algorithm to find the best matching structure of the input web pages. A kind of linear regression method is employed to get the weight of different tag-matching. Based on the alignment, we merge the trees into one union tree whose nodes record the statistical information gotten from multiple web pages. We use a transfer learning method to find the most likely content block and use the alignment algorithm to detect the repeat patterns on the union tree. After that, we generate a wrapper to extract data from web pages. Experimental results show that the method can achieve high extraction accuracy and has steady performance.