[Retracted] Knowledge Graph Entity Similarity Calculation under Active Learning

Abstract

To address the objectives of the adaptive learning platform, the requirements of the system in terms of business, functionality, and performance are mainly analysed, and the design of functions and database is completed; then, an updatable learner model is constructed based on the cognitive diagnosis model and resource preference attributes; then, the construction of the knowledge map is completed based on embedding to achieve knowledge point alignment, and based on this, the target knowledge points of learners are located with the help of deep learning; at the same time, the target knowledge points are taken as the starting point to generate the best learning path by traversing the knowledge map, and the corresponding learning resources and test questions are recommended for them with the help of the architecture; finally, the adaptive learning platform is developed in the environment using the architecture. Also, the target knowledge point is used as the starting point to traverse the knowledge map to generate the best learning path, and the corresponding learning resources and test questions are recommended for the learner in combination with the learner model; finally, this study adopts an architecture for the development of an adaptive learning platform in the environment to realize online tests, score analysis, resource recommendation, and other functions. A knowledge graph fusion system supporting interactive facilitation between entity alignment and attribute alignment is implemented. Under a unified conceptual layer, this system can combine entity alignment and attribute alignment to promote each other and truly achieve the final fusion of the two graphs. Our experimental results on real datasets show that the entity alignment algorithm proposed in this paper has a great improvement in accuracy compared with the previous mainstream alignment algorithms. Also, the attribute alignment algorithm proposed in this paper, which calculates the similarity based on associated entities, outperforms the traditional methods in terms of accuracy and recall.

The impact of modern Internet technology on the teaching of American voice in colleges and universities

“…The differences in points of interest are reflected in learning time, learning type, and geographical location. Therefore, in this paper, the similarity of interest points is modeled as a linear representation of the similarity of these 3 [17]. Thus the similarity between interest points sim 𝑃𝑂𝐼 is shown in equation ( 9):…”

Innovative Path of Music Education Teaching in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

“…It aims to play an important technical role in educational semantic computing, semantic correlation of teaching content, teaching model architecture, comprehensive platform establishment, development of intelligent question-answering system, learning personalized service, etc. [11][12]. Subject knowledge mapping will profoundly affect the process of teaching and learning, accurately assess the performance of teaching and learning, and promote the teaching of students according to their abilities so as to improve their innovative spirit and practical ability.…”

“…Jiexia Wu. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13] …”

Learning Factors Knowledge Tracing Model Based on Dynamic Cognitive Diagnosis

“…In the early research and application, researchers proposed traditional measurement theories, such as classical test theory [1], generalization theory [2], and item response theory [3]. e new generation of measurement theory [4][5][6] represented by cognitive diagnosis aims to measure the knowledge processing process within learners, so as to analyze the knowledge state and skill level of students at the microlevel. Considering the dynamic characteristics of learners' learning activities, the researchers further proposed the knowledge tracing model [7,8] in order to describe the dynamic change process of learners' knowledge ability.…”