Resource Classification and Knowledge Aggregation of Library and Information Based on Data Mining

Abstract

The traditional knowledge service systems have nonuniform data structures. Some data are structured, while some are semi-structured and even non-structured. Big data technology helps to optimize the integration and retrieval of the massive data on library and information (L&I), making it possible to classify the resources and optimize the configuration of L&I resource platforms according to user demand. Therefore, this paper introduces the new information service model of big data resources and knowledge services to the processing of L&I data. Firstly, the data storage structure and relationship model of the L&I resource platform were established, and used to sample and integrate the keywords of resource retrieval. Next, an L&I resource classification model was constructed based on support vector machine (SVM), and applied to extract and quantify the attributes of the keywords of resource retrieval. After that, a knowledge aggregation model was developed for a complex network of multiple L&I resource platforms. Experimental results demonstrate the effectiveness of the proposed knowledge aggregation model. The research findings provide a reference for the application of data mining in resource classification.