The growing demand for the meaningful use of electronic medical records has led to great interest in medical entities and relation extraction technologies. Most existing methods perform relation extraction based on manually labeled documents and rarely consider incorporating knowledge graphs that include rich, valuable structured knowledge, which will cause semantic ambiguities. To address this problem, we propose a knowledge-enhanced relation extraction (KERE) model. First, we extract knowledge information from the knowledge graph to generate a knowledge-guided word embedding. Then, the lexical features are considered supplementary information for semantic understanding. Experiments on real-world datasets show that the KERE model achieves important improvements in a biomedical relation extraction task.

中文翻译：

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中文 EMR 的知识增强关系提取

对有意义地使用电子病历的需求不断增长，引起了对医疗实体和关系提取技术的极大兴趣。大多数现有方法基于手动标记的文档执行关系提取，很少考虑合并包含丰富、有价值的结构化知识的知识图，这会导致语义歧义。为了解决这个问题，我们提出了一种知识增强关系提取（KERE）模型。首先，我们从知识图中提取知识信息以生成知识引导的词嵌入。然后，词汇特征被认为是语义理解的补充信息。在真实世界数据集上的实验表明，KERE 模型在生物医学关系提取任务中取得了重要的改进。