Web Knowledge Fusion System Based on Certainty Factor Theory and its Application
基于置信度理论的网络知识融合系统和应用

As a result of heterogeneity of network knowledge resources, knowledge fusion systems have to integrate and combine the multi-source data and information and reduce the ambiguity. Inconsistency and incompleteness leads to uncertainty in the knowledge fusion procedure due to complexity and ambiguity of the knowledge science. A web knowledge fusion system based on certainty factor theory is proposed, making up the defect on uncertainty processing of traditional knowledge systems. The subjectivity of the initial setup of the certainty factor is reduced by the feedback and self-adaption mechanism. The system is then applied to the online drug subject tracking problem, which solves the redundancy and contradiction in the drug subject fusion procedure and provides reliable drug subject information for the online drug supervisory board.