C-Rank:一种Deep Web数据记录可信度评估方法

针对Web信息可信度问题,提出了一种为Deep Web数据记录计算可信度的有效方法C-Rank。该方法为每一条记录构造一个S-R可信度网络,包含两种类型顶点及三种类型边。首先基于可信度传播的思想,利用顶点出度为每一个顶点计算其局部可信度值;再利用Record顶点入度及相邻Site顶点的可信度值,为该Record顶点计算权值;继而求得整个S-R网络的全局可信度值。实验证明,C-Rank方法能够合理而有效地评价数据记录的可信度,从而达到甄别虚假信息,为用户推荐可信数据记录的目的。该方法普遍适用于Deep Web的各个领域。

C-Rank: A Credibility Evaluation Method for Deep Web Records

How to identify and evaluate information credibility ranking has become an increasing important problem. To address the issue， an effective credibility evaluation method called C-Rank to compute trust values of records in Deep Web databases is proposed， which constructs an S-R credibility graph for each record. The graph contains 2 types of vertices and 3 types of edges. Firstly， each vertex’s local trust value is computed by using out-degrees based on the idea of trust propagation. Then， the weight of record vertex is computed by using its in-degree and adjacent site vertices’ local trust values. Lastly， the global trust value of the S-R credibility graph is computed， which denotes the record’s credibility in the whole Web. Experiment results show C-Rank can evaluate credibility rankings of records appropriately and discriminate false information effectively. This method is generally applicable to all domains of Deep Web.