A New Learning Resource Retrieval Method Based on Multi-knowledge Association Mining

Abstract

Ever since the human society has entered the era of big data, the quantity and type of digital learning resources on the Internet are increasing exponentially, and the requirement of students for learning resource retrieval is on the rise. However, the existing methods of learning resource retrieval generally overlook the overall knowledge systems that the students have already possessed, so it’s impossible for them to predict the students’ learning path or perform deviation adjustment. In view of these issues, this paper aims to study a new learning resource retrieval method based on multi-knowledge association mining. At first, the paper introduces the application of the Knowledge Graph Embedding (KGE) technology in learning resource retrieval, proposes the problem of learning resource retrieval, and points out the goal of learning resource retrieval. Then, a breadth-first soft-matching search algorithm is introduced to attain the multiple association paths between students and learning resources, and a retrieval module is constructed based on association path to further learn the association paths between students and learning resources within the framework of feature learning, and to predict the probability of interactions between students and learning resources. At last, this paper evaluates the association paths and uses experimental results to verify the validity of the proposed learning resource retrieval method.