LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
Methods Mol Biol. 2021;2190:289-305. doi: 10.1007/978-1-0716-0826-5_14.
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.
利用不同的信息源来支持生物医学概念之间关系的自动提取，有助于我们加深对生物系统的理解。这些关系的主要综合来源是生物医学文献。已经提出了几种关系提取方法来识别生物医学文献中的概念之间的关系，即使用神经网络算法。使用由多种数据表示形式组成的多通道架构，如深度神经网络，正在取得最先进的结果。数据表示形式的正确组合最终可以使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实体的语义和祖先信息，起着至关重要的作用。已经证明，将生物医学本体论纳入其中可以提高以前的最先进水平。
