Abstract
Most existing methods of Web data extraction realize goals based on DOM tree analysis or wrapper building. However, applicability and efficiency of these methods need to be further improved. According to the amount of information, Web pages will be divided into two structure types which are 1:1 and 1:N type respectively. As same type of Web pages has similar structure features, the paper proposes an approach of two-phase Web data extraction base on structure feature. In the phase of samples learning, structure feature and depository rules of Web pages are obtained according to the text feature of sample pages. In the phase of information extraction, Web data extraction is implemented by matching the page to be extracted with depository rules in knowledge base. Experimental results show that the approach proposed in the paper has well applicability and high efficiency.
This work is supported by “The Fundamental Research Funds for the Central Universities” (N100304003), “National Natural Science Foundation of China” (61073062), and “Natural Science Foundation of LiaoNing Province” (20102060).
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Anxiang, M., Kening, G., Xiaohong, Z., Bin, Z. (2011). Web Data Extraction Based on Structure Feature. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23235-0_75
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DOI: https://doi.org/10.1007/978-3-642-23235-0_75
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