The existing interactive graph search methods are mainly limited to optimize the annotation cost of single data. To solve this problem, this paper introduces a knowledge-driven method of modeling prior probability information for batch data annotation tasks which are more common in real scenes. This method extracts knowledge between entities of batch data and guides machine algorithms, thus reducing the cost of interactive graph search on the whole. The results of experiments on real datasetsverifies the superiority of proposed algorithm in terms of interaction efficiency compared with existing methods.

%U https://xbna.pku.edu.cn/EN/10.13209/j.0479-8023.2023.049