Generating networked knowledge based on statistical modeling and data mining techniques and its applications

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We aim to construct networked knowledge from scattered pieces of information using techniques of statistical modeling and data mining, and apply that knowledge to solve problems in human intellectual activities. To achieve these objectives, we mainly carried out the following research tasks:(1) Research on extracting relational structures from text data using topic models : We statistically estimated latent topics from some sorts of structured text data, and used the latent topics in some applications, such as extracting relational structures between entities or between data.(2) Research on inferring node clusters from network data and its applications : We statistically estimated latent communities from a network, and applied the estimated communities to real-world problems, such as link prediction.(3) Research on discovering patterns from network data : We developed algorithms to discover characteristic patterns from complex structured data, such as a single network in which nodes or edges are associated with a set of numerical attributes.