Using google distance for query expansion in expert finding
Kai-Hsiang Yang, Yu–Li Lin, Cheng-Tao Chuang · 2014
Expert finding, which identifies people with relevant knowledge or skills, is one of the most important issues under many circumstances. In this paper, we propose a method that utilizes Normalized Google Distance (NGD) with some global factors to enhance the relevance between initial query and extended query, and to improve the accuracy of the search results of the expert finding system. Results of a numerical study show that the NGD-based method has a higher accuracy than the methods proposed in the literature, and that the NGD-based method is more effective as the number of top results, N, increases. Moreover, the precision rate of our NGD-based method is, on average, higher than that of the other methods in the literature by 5%.