In recent years, there has been an increasing number of frameworks developed for biomedical entity and relation extraction. This research effort aims to address the accelerating growth in biomedical publications and the intricate nature of biomedical texts, which are written for mainly domain experts. To handle these challenges, we develop a novel framework that utilizes external knowledge to construct a task-independent and reusable background knowledge graph for biomedical entity and relation extraction. The design of our model is inspired by how humans learn domain-specific topics. In particular, humans often first acquire the most basic and common knowledge regarding a field to build the foundational knowledge and then use that as a basis for extending to various specialized topics. Our framework employs such common-knowledge-sharing mechanism to build a general neural-network knowledge graph that is learning transferable to different domain-specific biomedical texts effectively. Experimental evaluations demonstrate that our model, equipped with this generalized and cross-transferable knowledge base, achieves competitive performance benchmarks, including BioRelEx for binding interaction detection and ADE for Adverse Drug Effect identification.

翻译：近年来，为生物医学实体与关系抽取开发的框架日益增多。这项研究旨在应对生物医学文献的快速增长以及生物医学文本的复杂性，这些文本主要面向领域专家撰写。为应对这些挑战，我们开发了一种新颖框架，利用外部知识构建独立于任务且可复用的背景知识图谱，用于生物医学实体与关系抽取。我们的模型设计灵感来源于人类学习领域特定主题的方式。具体而言，人类通常首先获取某个领域最基础、最通用的知识以建立基础知识体系，随后以此为基础扩展至各类专业主题。本框架采用此类通用知识共享机制，构建了一个通用的神经网络知识图谱，该图谱能够有效学习并迁移至不同领域特定的生物医学文本。实验评估表明，配备这种广义且可跨领域迁移知识库的模型在多个性能基准测试中取得了具有竞争力的结果，包括用于结合相互作用检测的BioRelEx和用于药物不良反应识别的ADE。