关键词: 概率图模型, 主题模型, 马尔科夫逻辑网, 异构信息网络

Abstract: To solve the diversity of the data object type and the variety of the linkage relationship in the heterogeneous information network, this paper combines multiple topic models and a Markov logic network, and proposes a fusion probabilistic graphical model. The multiple topic models describe the distributions of the topic in the multiple data object subspaces, and preprocess the multiple data objects. Markov logic network connects the different data objects in each topic model using concatenation rules described by the first order logic clause. The algorithm learns the parameters and does the inference on the heterogeneous information network by using Gibbs sampling. The experimental results on the international public heterogeneous information network dataset DBLP indicate that employing the fusion probabilistic graphical model can represent the diversity of the data objects and the linkages. By compared with four typical classification methods, the experiment acquires the stable results and obtains the improved performance on the classification of the author, the paper and the conference.

Key words: probabilistic graphical model, topic model, Markov logic network, heterogeneous information network