Abstract
A large amount of drug and disease research knowledge is scattered in unstructured literature data, presenting significant challenges in text mining within the field of biomedicine. These challenges include handling professional knowledge, integrating related knowledge, and disambiguating different meanings of the same words. Therefore, constructing a biomedical knowledge graph can significantly save expert human resources and make efficient use of medical literature resources. This review paper aims to summarize the construction methods used during the development of Biomedical Knowledge Graphs. It also outlines the latest models and frameworks, such as BioBERT and LSTM+CRF, highlighting their contributions and applications. In addition, this paper points out the limitations of current biomedical knowledge graphs, such as scalability issues and the need for large annotated datasets. To address these limitations, it proposes the use of Apache Spark for improved processing capabilities and transfer learning to enhance model performance and adaptability in diverse biomedical contexts.
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