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

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