Application of Cloud Models in Digital Libraries
Haiyan Kang, Yanfang Li, Shiping Tang · 2007
In digital libraries, the traditional retrieval methods cannot efficiently retrieve uncertain concepts, and traditional evaluation methods have disadvantages on algorithm measures. To overcome above disadvantages, this paper proposes two new methods based on cloud models. One is to retrieve efficiently uncertain concepts, which can change the granularity of retrieval information. By this method it remarkably improves the recall and precision of information retrieval. Another is to evaluate efficiently retrieval algorithm, which can reflect not only average performance of an algorithm but also stability and randomicity. These two methods set up a transform of qualitative concepts and quantity. This kind of transform is carried out through strict mathematic means. Experimental data showed the method is practical. Results of evaluation will be more accurate and approach to the fact better.