Abstract
With the rapid growth of biomedical documents, finding out interested documents efficiently becomes a challenging for researchers. To improve the efficiency and display biomedical information in a more direct way, a medical knowledge graph-based semantic text analysis tool is developed. This tool is based on improved bag-of-words and ontology-based semantic text mining algorithms, supporting visualized biomedical conception and documents analysis. The testing results show that proposed models perform well on medical documents clustering, accuracy in different dataset is above 82% and the best one reaches 96%.
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