An intelligent state machine towards task-oriented search support

Researchers tend to agree that an increasing quantity of data has caused the complexity and difficulty for information discovery, management and reuse. An essential factor relates to the increasing channels for information sharing. Finding information, especially those meaningful or useful one, that meets ultimate task of user becomes harder then it is used to be. In this research, issues concerning the use of user-generated contents for individual search support are investigated. In order to make efficient use of user-generated contents, an intelligent state machine, as a hybridization of graph model and petri-net model (i.e. Document Sensitive Petri-Net), is proposed. It is utilized to clarify the vague usage scenario between user-generated contents, such as discussions, posts, etc., and to identify correlations and experiences within them. As a practical contribution, an interactive search algorithm that generates potential solutions for individual is implemented. The feasibility of this research is demonstrated by a series of experiments and empirical studies with around 320,000 user-generated contents collected from the Internet and 180 users.