[Objective] This paper studied the linked data from the Web, which is machine-readable, semantically meaningful and relationally descriptive. We examined these data’s effectiveness to improve the information organization of the academic resource websites (ARWs), with the purpose of retrieving more similar documents. [Methods] We first calculated the similarity of documents published in the ARWs with the help of the Latent Semantic Analysis (LSA) method. Then, chose documents with high similarities by the Hierarchical Cluster method, and created a document relation matrix. Finally, we used the dynamic document technology to generate a linked data index to search the ARWs. [Results] We built a preliminary ARWs linked data index, which helped us find similar documents more effectively from the ARWs. [Limitations] We investigated the similar documents retrieval technology from the perspective of statistical analysis. Therefore, further research is needed to locate similar documents from various subject areas with the support of deep learning technology. [Conclusions] We computed documents’ similarity using LSA method to discover related documents of specific articles. The linked data could help us find more similar documents, while reducing the waiting time for similarity calculation.
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