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社区知识图表抽象用于增强链接预测:对PubMed知识图表的研究

Yang Zhao¹, Danushka Bollegala², Shunsuke Hirose¹

¹Deloitte Analytics R&D, Deloitte Touche Tohmatsu LLC, 3-2-3 Marunouchi, Chiyoda-ku, Tokyo, 100-8360, Japan.

Journal of biomedical informatics

|September 12, 2024

概括

本研究引入了知识图嵌入 (KGE) 模型的扩展,用于推断缺失的生物医学知识. 这种新的方法提高了社区知识图 (CKG) 上的链接预测准确性,改善了生物医学数据分析.

科学领域:

生物医学信息学生物医学信息学
人工智能的人工智能
自然语言处理自然语言处理.

背景情况:

生物医学知识正在迅速扩大,对现有的知识图 (KG) 进行压倒性的手册更新.
现有的知识图嵌入 (KGE) 方法很难在KG中捕捉属性特定的实体关系.

研究的目的:

提出KGE模型的扩展方法,以改善生物医学KG中的链接预测.
解决当前KGE方法在捕捉复杂实体关系方面的局限性.

主要方法:

开发了一个基于实体距离的方法,从PubMed知识图 (PKG) 中抽象社区知识图 (CKG).
扩展了现有的KGE模型,将PKG信息集成到抽象的CKG中,用于链接预测.
使用TransE,TransH,DistMult,ComplEx,SimplE和RotatE模型评估性能,使用MR,MRR和Hits@k等指标.

主要成果:

拟议的扩展改善了所有六个评估的KGE模型的链接预测准确性.
前十名的准确性显著增加,例如,RotatE.的准确性从0.76增加到0.85.
结果表明扩展方法的广泛适用性和有效性.

结论:

该研究提出了一种用于抽象CKG和增强KGE性能的新方法.
扩展方法显示了生物医学KG的链接预测的显著改进.
未来的工作包括将链接预测应用于PKG中新引入的实体.

关键词:

回溯的过程是回溯的过程.在CKG中使用.基于实体距离的方法 KGE KGE 在线观看链接预测链接预测这是PKG PKG.

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