The Research of Building Multidimensional Multi-granularity Automatic Uncertain Knowledge System Based on Attributes Similarity

Network resources are fully rich, but the source of information is uneven. Huge amount of information, complex diversity of the network information and vastly different perspectives has brought great distress for people to identify information [1]. Due to the complexity of objective things, uncertainty and ambiguity of human thinking and other factors, the actual decision-making information is often difficult to quantify. General choice is to express the decision-making information in the form of qualitative language, but this multi-language form depends on human mind. What's more, different decision-makers will be based on their existing personal experience or cognition degree on the same issue to make good and bad, individualized decision-making, thereby it increases the uncertainty in decision making and labor costs in decision-making process. In this paper, considering the entirety of information on the whole and the drawback of the information on the local, we combine the human knowledge with machine algorithm. The method proposed in this paper is based on the similarity degree of the research object's attributes and categories, which uses the ideas of information fusion [2], make good use of a variety of information together and in view of multidimensional multi-granularity information to confirm each other to find a more effective method to distinguish the similarity measure of information object.