Relation extraction in the biomedical domain is challenging due to the lack
of labeled data and high annotation costs, needing domain experts. Distant
supervision is commonly used to tackle the scarcity of annotated data by
automatically pairing knowledge graph relationships with raw texts. Such a
pipeline is prone to noise and has added challenges to scale for covering a
large number of biomedical concepts. We investigated existing broad-coverage
distantly supervised biomedical relation extraction benchmarks and found a
significant overlap between training and test relationships ranging from 26% to
86%. Furthermore, we noticed several inconsistencies in the data construction
process of these benchmarks, and where there is no train-test leakage, the
focus is on interactions between narrower entity types. This work presents a
more accurate benchmark MedDistant19 for broad-coverage distantly supervised
biomedical relation extraction that addresses these shortcomings and is
obtained by aligning the MEDLINE abstracts with the widely used SNOMED Clinical
Terms knowledge base. Lacking thorough evaluation with domain-specific language
models, we also conduct experiments validating general domain relation
extraction findings to biomedical relation extraction.

本研究针对生物医学领域中关系抽取的挑战，提出利用知识图谱关系对原始文本进行自动标注，以克服标注数据缺乏的问题，并通过构建更为准确的基准集 “MedDistant19” 来解决现有基准集存在的一系列问题，并验证了其在领域中具有普遍适用性。