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.
翻译:利用不同的信息来源,支持生物医学概念之间关系的自动提取,有助于发展我们对生物系统的理解。这些关系的主要全面来源是生物医学文献。提出了几种关系提取方法,以确定生物医学文献中的概念之间的关系,即使用神经网络算法。利用由多种数据表述组成的多渠道结构,如深层神经网络,正在导致最先进的结果。正确的数据表述组合最终会使我们在提取任务方面获得更高的评价分数。因此,生物医学本体学通过提供有关实体的语义学和祖先信息发挥着根本作用。生物医学本体学的结合已经证明加强了以往的最新结果。