In this work, we propose a method for the automated refinement of subject annotations in biomedical literature at the level of concepts. Semantic indexing and search of biomedical articles in MEDLINE/PubMed are based on semantic subject annotations with MeSH descriptors that may correspond to several related but distinct biomedical concepts. Such semantic annotations do not adhere to the level of detail available in the domain knowledge and may not be sufficient to fulfil the information needs of experts in the domain. To this end, we propose a new method that uses weak supervision to train a concept annotator on the literature available for a particular disease. We test this method on the MeSH descriptors for two diseases: Alzheimer’s Disease and Duchenne Muscular Dystrophy. The results indicate that concept-occurrence is a strong heuristic for automated subject annotation refinement and its use as weak supervision can lead to improved concept-level annotations. The fine-grained semantic annotations can enable more precise literature retrieval, sustain the semantic integration of subject annotations with other domain resources and ease the maintenance of consistent subject annotations, as new more detailed entries are added in the MeSH thesaurus over time.

在这项工作中，我们提出了一种在概念层面上自动完善生物医学文献中主题注释的方法。MEDLINE / PubMed中生物医学文章的语义索引和搜索是基于带有MeSH描述符的语义主题注释，该注释可能对应于几个相关但截然不同的生物医学概念。这样的语义注释不符合领域知识中可用的详细程度，并且可能不足以满足领域专家的信息需求。为此，我们提出了一种新的方法，该方法使用弱监督来训练可用于特定疾病的文献上的概念注释器。我们在MeSH描述符上针对两种疾病测试了该方法：阿尔茨海默氏病和杜兴氏肌营养不良症。结果表明，概念出现是自动主题注释细化的一种强力启发法，由于它的使用不力，可以改善概念级别的注释。细粒度的语义注释可以实现更精确的文献检索，保持主题注释与其他领域资源的语义集成，并随着时间的推移在MeSH同义词库中添加新的更详细的条目而简化对主题注释的维护。