Application Of Extended Belief Network Model For Scientific Document Retrieval

Reference is a part of important information for scientific document. It presents the basic knowledge of document and shows the information of other scientific document which is related to this one. The traditional information retrieval systems merely use content information and not relate to reference information while computing.Base on this phenomenon, this paper compute the important degree of document based on the reference information. Reference evidence is combined with the original belief network model. Then we get the extended model which combines content information evidence and reference information evidence. This paper introduces an algorithm to compute the two metrics: worth and concern, which are used to measure the important degree of document based on the reference evidence. The metrics are rationalized by Hermann Ebbinghaus curve of forgetting. The model of extended belief network is presented. The way to express a document, probability computation and the whole inference process are described in detail. Finally, new model is used in examination and the result shows that extended model gets better retrieval performance than original belief network model.