Retrieval method of network education resources based on associated data

Aiming at the problems of low retrieval accuracy and long retrieval time in traditional online education resource retrieval methods, a method of online education resource retrieval based on associated data is proposed. First, establish a model of online education resources, obtain online education resource data through web crawlers, and then pre-process the obtained online education resource data through a series of steps. Then, based on the pre-processing results, construct a semantic grid computing model to extract semantic features of online education resources. Finally, based on semantic features, associate the semantic data of online education resources through semantic similarity and semantic correlation, Based on the associated data, a retrieval model for online education resources is established using naive Bayesian methods to obtain the retrieval results. Simulation results show that the proposed method has higher retrieval accuracy and shorter retrieval time for online education resources.

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Publication Details

Journal
International Journal of Reasoning-based Intelligent Systems
Published
2025-01-01
DOI
https://doi.org/10.1504/ijris.2025.147454
Primary Topic
Educational Technology and Assessment
Type
article
Field-Weighted Citation Impact
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article

Retrieval method of network education resources based on associated data

Guojuan Li
International Journal of Reasoning-based Intelligent Systems
Educational Technology and Assessment
article

Retrieval method of network education resources based on associated data

Guojuan Li
article en

Abstract

Aiming at the problems of low retrieval accuracy and long retrieval time in traditional online education resource retrieval methods, a method of online education resource retrieval based on associated data is proposed. First, establish a model of online education resources, obtain online education resource data through web crawlers, and then pre-process the obtained online education resource data through a series of steps. Then, based on the pre-processing results, construct a semantic grid computing model to extract semantic features of online education resources. Finally, based on semantic features, associate the semantic data of online education resources through semantic similarity and semantic correlation, Based on the associated data, a retrieval model for online education resources is established using naive Bayesian methods to obtain the retrieval results. Simulation results show that the proposed method has higher retrieval accuracy and shorter retrieval time for online education resources.

International Journal of Reasoning-based Intelligent SystemsVol. 17(3)
Shijiazhuang University (CN)
Quality Education
Openalex Percentile: Top 74%
Educational Technology and Assessment
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