Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via Graph Edge-conditioned Attention Networks (GEANet) and hierarchical graph representation. To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.

中文翻译：

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使用分层知识图进行生物医学事件提取

生物医学事件提取对于理解科学语料库中描述的生物分子相互作用至关重要。主要挑战之一是识别与非指示性触发词相关的嵌套结构化事件。我们建议通过图边缘条件注意网络 (GEANet) 和分层图表示将来自统一医学语言系统 (UMLS) 的领域知识整合到预训练的语言模型中。为了更好地识别触发词，每个句子首先基于来自 UMLS 的联合建模分层知识图的句子图。然后通过 GEANet 传播接地图，GEANet 是一种新颖的图神经网络，用于增强推断复杂事件的能力。在 BioNLP 2011 GENIA 事件提取任务中，我们的方法达到了 1.41% F1 和 3。F1 对所有事件和复杂事件的改进分别为 19%。消融研究证实了 GEANet 和分层 KG 的重要性。