Abstract
Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.
Abstract (translated)
利用不同的信息源支持生物医学概念之间关系的自动提取，有助于我们对生物系统的理解。这些关系的主要综合来源是生物医学文献。提出了几种利用神经网络算法识别生物医学文献中概念间关系的方法。像在深度神经网络中一样，使用由多个数据表示组成的多通道体系结构将产生最先进的结果。数据表示的正确组合最终会导致我们在关系提取任务中获得更高的评估分数。因此，生物医学本体论通过提供一个实体的语义和祖先信息发挥了基本作用。生物医学本体的结合已经被证明可以增强以前的最新成果。
URL
https://arxiv.org/abs/1905.11391
