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The burgeoning field of biomedical research grapples with a flood of heterogeneous data, necessitating robust systems for integration and insight extraction. Knowledge graphs, though powerful, remain un derutilized in bio-medicine. We propose a method that leverages multi threading to enrich knowledge graphs with semantic entities from the BioKG dataset. This integration presents a wealth of opportunities, in cluding more accurate predictions, deeper insights, and enhanced functionality such as advanced language models for biomedical entities and intelligent search capabilities. Our endeavors culminated in the success ful crawling of 115,565 biomedical entities, encompassing proteins, drugs, and diseases, thus furnishing researchers with readily accessible resources for training knowledge graphs.

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A Multi-threaded Framework for Integrating Knowledge into a Biomedical Knowledge Graph

  • 会议论文

  • 发表日期：

    2026年

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  • Le Van Thanh,
  • Cu Kim Long,
  • Duong Vu Tuan Minh,
  • Nguyen Hai Anh,
  • Tran Minh Dung,
  • Pham Van Hai

摘要

The burgeoning field of biomedical research grapples with a flood of heterogeneous data, necessitating robust systems for integration and insight extraction. Knowledge graphs, though powerful, remain un derutilized in bio-medicine. We propose a method that leverages multi threading to enrich knowledge graphs with semantic entities from the BioKG dataset. This integration presents a wealth of opportunities, in cluding more accurate predictions, deeper insights, and enhanced functionality such as advanced language models for biomedical entities and intelligent search capabilities. Our endeavors culminated in the success ful crawling of 115,565 biomedical entities, encompassing proteins, drugs, and diseases, thus furnishing researchers with readily accessible resources for training knowledge graphs.