Deep Web Entity Identification Method Based on Improved Jaccard Coefficients
Yu Wang, Yinghua Li · 2009
There are a large number of accessible deep Web sites on the Internet. However, even if identical entity has different representation formats on different Web sites. So entity identification plays a crucial role in deep Web data mining. This paper proposes an entity identification method in the field of Chinese books. First, using improved Jaccard coefficients to calculate similarity of text attributes. Second, AHP (analytic hierarchy process) is used to obtain the weights, and using the sum of weights to calculate the entity similarity. Finally, it needs to integrate duplicate entity to achieve the entity identification. The experiment results demonstrate the approach has higher accuracy with good feasibility.