Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such as domain-specific ontologies. However, using deep learning methods, along with biomedical ontologies, has been recently shown to effectively advance the biomedical relation extraction field. To perform relation extraction, our deep learning system, BiOnt, employs four types of biomedical ontologies, namely, the Gene Ontology, the Human Phenotype Ontology, the Human Disease Ontology, and the Chemical Entities of Biological Interest, regarding gene-products, phenotypes, diseases, and chemical compounds, respectively. We tested our system with three data sets that represent three different types of relations of biomedical entities. BiOnt achieved, in F-score, an improvement of 4.93 percentage points for drug-drug interactions (DDI corpus), 4.99 percentage points for phenotype-gene relations (PGR corpus), and 2.21 percentage points for chemical-induced disease relations (BC5CDR corpus), relatively to the state-of-the-art. The code supporting this system is available at https://github.com/lasigeBioTM/BiOnt.

中文翻译：

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BiOnt：使用多个生物医学本体进行关系提取的深度学习

成功的生物医学关系提取可以为研究人员和临床医生提供关于生物医学实体之间可能未知关联的证据，推进我们目前对这些实体及其内在机制的了解。大多数生物医学关系提取系统不求助于外部知识来源，例如特定领域的本体。然而，使用深度学习方法以及生物医学本体最近已被证明可以有效地推进生物医学关系提取领域。为了执行关系提取，我们的深度学习系统 BiOnt 采用了四种类型的生物医学本体，即基因本体、人类表型本体、人类疾病本体和生物学感兴趣的化学实体，关于基因产物、表型、疾病和化合物，分别。我们用三个数据集测试了我们的系统，这些数据集代表了生物医学实体的三种不同类型的关系。BiOnt 在 F-score 中，药物-药物相互作用（DDI 语料库）提高了 4.93 个百分点，表型-基因关系（PGR 语料库）提高了 4.99 个百分点，化学诱导疾病关系（BC5CDR 语料库）提高了 2.21 个百分点)，相对于最先进的技术。支持该系统的代码可在 https://github.com/lasigeBioTM/BiOnt 获得。化学诱导疾病关系（BC5CDR 语料库）的 21 个百分点，相对于最先进的技术。支持该系统的代码可在 https://github.com/lasigeBioTM/BiOnt 获得。化学诱导疾病关系（BC5CDR 语料库）的 21 个百分点，相对于最先进的技术。支持该系统的代码可在 https://github.com/lasigeBioTM/BiOnt 获得。