BACKGROUND Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge. RESULTS The paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining. CONCLUSION The experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts.

中文翻译：

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基于关注的外部知识增强的生物语义关系提取。

背景技术诸如知识库的语义资源包含高质量结构化的知识，因此需要领域专家的大量努力。使用资源加强从非结构化文本的信息检索可以进一步利用这种非结构化文本资源及其策划知识的潜力。结果本文提出了一种新颖的方法，该方法使用一种采用了先验知识的深度神经网络模型来提高从科学文献中自动提取生物语义关系的性能。该模型基于将注意力机制与语义资源（即UniProt和BioModels）结合的递归神经网络。我们的方法是在BioNLP和BioCreative语料库（一组手动注释的生物学文本）上进行评估的。实验表明，该方法优于当前的最新模型，结构化的语义信息可以改善生物文本挖掘的效果。结论实验结果表明，该方法可以有效利用外部先验知识信息，提高蛋白质相互作用提取任务的性能。尽管该方法已在生物医学文献中得到验证，但仍应能够推广到其他类型的数据。