通过合并实体描述和类型信息来完成医学知识图表
Xiaochen Wang1, Runtong Zhang1, Butian Zhao1
1Department of Information Management, Beijing Jiaotong University, Beijing, 100044, China.
Artificial intelligence in medicine
|April 24, 2024
概括
本研究介绍了一种基于BioBERT的模型,通过整合图形,语言和类型信息来改进医学知识图 (MKG). 新的框架增强了医学情报的知识图表完成.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
背景情况:
- 医疗知识图 (MKG) 对医疗保健中的大数据至关重要,但由于数据稀疏和缺失的事实而受到影响.
- 现有的知识嵌入方法往往忽略了丰富的实体属性信息,限制了复杂的MKG中的性能.
研究的目的:
- 提出一个基于BioBERT的图形嵌入模型,以解决MKG完成方面的局限性.
- 集成图形嵌入,语言嵌入和类型信息,以增强MKG实用程序.
主要方法:
- 开发了一个可发展的框架,结合了图形嵌入,语言嵌入 (BioBERT) 和类型信息.
- 使用WordNet和MedicalKG数据集进行评估和比较.
主要成果:
- 拟议的核聚变框架实现了与基线方法相比的最先进性能 (SOTA).
- 证明了对MKG的知识图表完成能力的改进.
结论:
- 基于BioBERT的融合框架为增强医学知识图表完成提供了一个有希望的方法.
- 为医疗智能和医疗保健中的大数据技术的未来进步提供了宝贵的见解.
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