MULTI-GRANULARITY KNOWLEDGE MINING ON THE WEB

Abstract

We tackle the problem of knowledge mining on the Web. In this paper, we propose MGKM algebraic system for iterative search documents sets, and then develop an approach to extract topics on the web with Multi-Granularity Knowledge Mining algorithm (MGKM). The proposed approach maps the data space of the original method to a vector space of sentence, improving the original DBCO algorithm. We outline the interface between our scheme and the current data Web, and show that, in contrast to the existing approaches, no exponential blowup is produced by the MGKM. Based on the experiments with real-world data sets of 310 users in three study sites, we demonstrate that knowledge mining in the proposed approach is efficient, especially for large-scale web learning resources. According to the user ratings data of four learning sites in the 150 days, the average rate of increase of user rating after the system is used reaches 25.18%.