Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The state prediction plays a key role in the evolution analysis of b events. There are two issues for the state prediction of b events: one is what factors impact on the state transition of b events; and the other is how the prior knowledge can guide the state transition of b events. For the first issue,  discuss two types of temporal features observed from the real time bpages 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 b events. As the prior knowledge, they represent the relations beten the states transition and the relations of unobserved space (i.e., the six states of b 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 b events. Experimental results show that the model has good performance and robustness because it combines the prior knowledge and the real time data of b events.
['Xiangfeng Luo', 'Junyu Xuan', 'Huimin Liu']
I am writing a research paper on a idea Web event state prediction model: combining prior knowledge with real time data
[{"from": "human", "value": "I am writing a research paper on a idea Web event state prediction model: combining prior knowledge with real time data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The state prediction plays a key role in the evolution analysis of b events. There are two issues for the state prediction of b events: one is what factors impact on the state transition of b events; and the other is how the prior knowledge can guide the state transition of b events. For the first issue,  discuss two types of temporal features observed from the real time bpages 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 b events. As the prior knowledge, they represent the relations beten the states transition and the relations of unobserved space (i.e., the six states of b 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 b events. Experimental results show that the model has good performance and robustness because it combines the prior knowledge and the real time data of b events."}]