Abstract
Based on the 5G wireless semantic network, this paper realizes the construction of the English digital learning resources query system, providing the query and management functions for English digital learning resources, and realizes the integration of functions and resources on the digital learning system and mobile learning system. The platform is put into practical application, and data analysis is conducted to conclude how this system is used and whether it is conducive to improving students’ interest in learning English and their English proficiency. We propose a quality algorithm for 5G wireless networks, which not only captures the characteristics of 5G wireless networks such as local broadcast and node movement but also combines quality predicates as guards with broadcast reception actions, which are used to portray that a node can continue to execute subsequent processes only when the node reception actions satisfy certain conditions, which works together with the node default value mechanism to reduce the impact caused by unreliable links. The query content is interpreted and supplemented at the semantic level by using synonym expansion, and the knowledge base is used to filter the synonym expansion and further expand the synonym expansion query based on knowledge structure, to tap the implicit knowledge points related to the query, and to fuse the two extensions to realize the knowledge-based query expansion. We use 5G wireless semantic web-related technologies to build an English digital learning resource query system with complete functions and good experience across terminals. Through the questionnaire survey and data analysis of the users of this system, it is found that the system is well used and effectively enhances students’ interest in learning English and improves learning efficiency. By applying the experimental comparison method, it is known that this system has improved students’ English proficiency to a certain extent.
Highlights
The single structure of these learning resources and the lack of semantic annotations make it impossible to share learning resources, which restricts the development of online education [5]. erefore, this paper applies the method of constructing a knowledge base to English digital learning resources query and, on this basis, uses the method of latent semantic analysis to establish the index of English digital learning resources to knowledge points and outputs English digital learning resources related to the query content by extracting knowledge points from the query content
Based on constructing a knowledge base to manage English digital learning resources, the semantic index based on knowledge structure of the knowledge base and the synonym information of the query content are fully utilized to find relevant knowledge points and realize knowledge-based query expansion and realize the expansion of query results
In addition to the management of English digital learning resources, the effectiveness of querying English digital learning resources is closely related to the needs of learners, and how to dig out the query intention of users through the query content input by learners has become one of the core researches in the field of querying at present
Summary
With the increasing reliance on computer networks, the network has become a basic tool indispensable for the normal operation of society and people’s regular work life. E extended query based on the conceptual semantic network can improve the query completeness and accuracy of teaching resources and provide learners with guided associative learning in the domain related to the queried knowledge point, which is the current development direction of teaching resources query [14]. E research related to querying English digital learning resources, constructing a knowledge ontology base, establishing a user interest model, and conducting contextawareness can all meet the query needs in specific situations to a certain extent, and constructing an ontology knowledge base is an effective means to realize semantic querying, which can realize query expansion of query contents [20]. E architecture of this system adopts this design model, and there are three roles of the system as follows: learners, View layer
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