An LDA based model for semantic annotation of Web English educational resources - Fei, Xue (Journal of intelligent & fuzzy systems, 2021)
With the development and application of Web Semantic, users are no longer satisfied with basic metadata descriptions such as titles and link texts and string-matching search results. They hope that the resource description can provide the theme ideas, topics, and topics involved in the resource. Potential semantic information contains documents such as teaching methods and knowledge-concept relationships. This research starts from the demand for semantic annotation of resources in the process of resource library construction and sharing, and uses the LDA model to semantically model the document resources in the resource library to mine potential topics in the document. From “document-topic-keyword” scheme, the semantic description of teaching resources at different levels enriches the metadata attributes and content of resources, and adds more related topics and corresponding keyword descriptions related to disciplines, teaching content, teaching methods, etc., providing resource retrieval and sharing. The experimental results show that the LDA model can catalogues teaching resources from a macro perspective, and model LDA on teaching subject resources of the same teaching content. It can mine the inherent semantic topic features and detailed differences of resources. The final performance analysis verifies LDA’s advantages of the model in parallel computing in the big data environment.