WEB EVENT STATE PREDICTION MODEL: COMBINING PRIOR KNOWLEDGE WITH REAL TIME DATA
Keywords:
web event, hidden Markov model, topic detection and tracking, multi-factor analysisAbstract
The state prediction plays a key role in the evolution analysis of web events. There are two issues for the state prediction of web events: one is what factors impact on the state transition of web events; and the other is how the prior knowledge can guide the state transition of web events. For the first issue, we discuss two types of temporal features observed from the real time webpages covering an event, i.e., the statistical ones and the knowledge structural ones. For the second issue, Fuzzy Cognitive Map (FCM) and conditional dependency matrix are mined from the training web events. As the prior knowledge, they represent the relations between the states transition and the relations of unobserved space (i.e., the six states of web events) and observed space (i.e., the two types of features). Based on that, an improved hidden Markov model is developed to predict the state transition of web events. Experimental results show that the model has good performance and robustness because it combines the prior knowledge and the real time data of web events.
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