中文摘要：

专家证据文档识别是专家检索的关键步骤．融合专家候选文档独立页面特征以及页面之间的关联关系，提出了一个专家证据文档识别无向图模型．该方法首先分析各类专家证据文档中的词、URL链接、专家元数据等独立页面特征以及候选专家证据文档间的链接和内容等关联关系：然后将独立页面特征以及页面之间的关联关系融入到无向图中构建专家证据文档识别无向图模型；最后利用梯度下降方法学习模型中特征的权重，并利用吉布斯采样方法进行专家证据文档识别．通过对比实验验证了该方法的有效性．实验结果表明，该方法有较好的效果．

英文摘要：

Expert evidence document recognition is the key step for expert search. Combining specialist candidate document independent page features and correlation among pages, this paper proposes an expert evidence document recognition method based on undirected graph model. First, independent page features such as words, URL links and expert metadata in all kinds of expert evidence document, and correlations such as links and content among candidate expert evidence document are analyzed. Then, independent page features and correlation among pages are integrated into the undirected graph to construct an undirected graph model for expert evidence document recognition. Finally, feature weights are learned in the model by using the gradient descent method and expert evidence document recognition is achieved by utilizing Gibbs Sampling method. The effectiveness of the proposed method is verified by comparison experiment. The experimental results show that the proposed method has a better effect.