Relation Extraction (RE) is the task of extracting semantic relationships between entities in a sentence and aligning them to relations defined in a vocabulary, which is generally in the form of a Knowledge Graph (KG) or an ontology. Various approaches have been proposed so far to address this task. However, applying these techniques to biomedical text often yields unsatisfactory results because it is hard to infer relations directly from sentences due to the nature of the biomedical relations. To address these issues, we present a novel technique called ReOnto, that makes use of neuro symbolic knowledge for the RE task. ReOnto employs a graph neural network to acquire the sentence representation and leverages publicly accessible ontologies as prior knowledge to identify the sentential relation between two entities. The approach involves extracting the relation path between the two entities from the ontology. We evaluate the effect of using symbolic knowledge from ontologies with graph neural networks. Experimental results on two public biomedical datasets, BioRel and ADE, show that our method outperforms all the baselines (approximately by 3\%).

翻译：关系抽取（Relation Extraction, RE）是从句子中提取实体之间的语义关系，并将其与知识图谱（Knowledge Graph, KG）或本体等词汇表中定义的关系对齐的任务。迄今为止，已有多种方法被提出用于解决该任务。然而，将这些技术应用于生物医学文本时，往往结果不佳，原因在于生物医学关系的本质使得从句子中直接推断关系较为困难。为了解决这些问题，我们提出了一种名为ReOnto的新技术，该技术利用神经符号知识来完成RE任务。ReOnto采用图神经网络获取句子表示，并利用公开可访问的本体作为先验知识来识别两个实体之间的句子级关系。该方法涉及从本体中提取两个实体之间的关系路径。我们评估了使用本体中符号知识与图神经网络相结合的效果。在两个公开的生物医学数据集BioRel和ADE上的实验结果表明，我们的方法在所有基线方法中表现最优（提升约3%）。