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.

From data to insights: the application and challenges of knowledge graphs in intelligent audit

“…This graphical representation not only allows people to easily understand the relations between information but also enables computers to efficiently process large amounts of data. In recent years, due to the outstanding advantage of knowledge graphs in representing structured data, they have played an increasingly important role in variousAI tasks, injecting new vitality into intelligent question answering [17], intelligent recommendation [18], and information retrieval [19]. Numerous large-scale knowledge graphs such as DBPedia [20], Freebase [21], WordNet [20], and Wikidata [22] are widely used in various fields.…”

A New Evolution Model for Collaborative Management of Knowledge Resources

“…Scholars in relevant research fields began to attach importance to the collaborative management of knowledge resources in recent decades and they have carried out a series of studies, most of which focuses on aspects of the classification, integration and sharing of knowledge resources, and a few of them on the role of knowledge attribute regulation [12][13][14][15]. In fact, knowledge attribute regulation plays a key role in the collaborative management of knowledge resources, as it helps organizations and individuals better acquire, integrate, and utilize knowledge resources, thereby improving the ability to innovate and apply knowledge [16][17][18].…”