基于隐马尔可夫模型的多真值发现算法

数据量的增长加大了信息获取的难度，如何从大量数据中准确获得有效信息是当前的研究热点。借鉴隐马尔可夫模型的状态转移概率，构建了基于图模型的多真值发现算法GraphTD，借助各数据源中描述的可信度转移矩阵，计算出数据值为真的概率的收敛值。同时，提出改进的初始真值的确定算法CVote，可有效提高GraphTD的正确率，避免了投票法在多真值发现中存在的诸多不足。在书籍作者数据集上的实验结果表明，基于图模型的GraphTD真值发现算法能够提高真值识别的准确率，CVote算法通过对初始真值选择的改良，可以有效提高真值发现算法的正确率。

Hidden Markov model based multi-truth discovery algorithm

The increase in data size has caused the difficulty of obtaining information. How to get accurate information from a large amount of data is a hot topic. Inspired by the Hidden Markov Model, a multi-truth discovery algorithm (Graph Truth Discovery, GraphTD) based on the graph model is proposed. With the help of the credibility transition matrix described in each data source, the probability that the data value is true is calculated. Meanwhile, an improved method for determining the initial true value is proposed, which can effectively improve the accuracy of GraphTD and avoid many shortcomings in the multi-truth discovery of the voting method. Experimental results on the book author dataset show that GraphTD can effectively improve the recognition accuracy of truth value, and CVote can significantly improve the discovery accuracy of truth value through the optimized selection strategy of initial truth value.