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.
Highlights
In today’s era of high-speed Internet development and explosive growth of information, people are prone to problems such as information overload, which makes it difficult to obtain effective information and learn knowledge
Recommendation systems have attracted the attention of many experts and scholars
Combining event-oriented knowledge graph construction and chapter-oriented knowledge graph construction, the process of knowledge graph construction and the algorithms involved in each process are analysed, the characteristics of each algorithm are analysed, and various parallelization methods are designed and applied according to the characteristics of the algorithms
Summary
In today’s era of high-speed Internet development and explosive growth of information, people are prone to problems such as information overload, which makes it difficult to obtain effective information and learn knowledge. Based on the idea of a collaborative filtering algorithm with fused contents, KG-CF directly fuses the distributed representation vector of items in the knowledge graph of the movie domain into the item similarity calculation; i.e., it supplements the semantic information of items to the traditional item-based collaborative filtering algorithm and improves the personalized recommendation effect. E algorithm selection is mainly considered from three aspects, namely, practicability, generality, and time complexity: practicability refers to the special algorithm that needs parallelization in the actual construction of a specific knowledge graph; generality refers to the parallel processing method of the algorithm that can be applied to other similar algorithms with slight modifications; time complexity refers to the algorithm that has a large amount of data or a large amount of computation, and the processing time of a single node needs to be measured in weeks or months Due to the complexity of the knowledge graph construction process, there are many solutions to a certain problem in each stage, and it is not possible to cover them all. erefore, we analyse the parallelization techniques of a specific algorithm in the knowledge graph construction process as an example for the parallelization of other similar algorithms for reference. e algorithm selection is mainly considered from three aspects, namely, practicability, generality, and time complexity: practicability refers to the special algorithm that needs parallelization in the actual construction of a specific knowledge graph; generality refers to the parallel processing method of the algorithm that can be applied to other similar algorithms with slight modifications; time complexity refers to the algorithm that has a large amount of data or a large amount of computation, and the processing time of a single node needs to be measured in weeks or months
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
