Data from: Utilizing descriptive statements from the Biodiversity Heritage Library to expand the Hymenoptera Anatomy Ontology

Hymenoptera, the insect order that includes sawflies, bees, wasps, and ants, exhibits an incredible diversity of phenotypes, with over 145,000 species described in a corpus of textual knowledge since Carolus Linnaeus. In the absence of specialized training, often spanning decades, however, these articles can be challenging to decipher. Much of the vocabulary is domain-specific (e.g., Hymenoptera biology), historically without a comprehensive glossary, and contains much homonymous and synonymous terminology. The Hymenoptera Anatomy Ontology was developed to surmount this challenge and to aid future communication related to hymenopteran anatomy, as well as provide support for domain experts so they may actively benefit from the anatomy ontology development. As part of HAO development, an active learning, dictionary-based, natural language recognition tool was implemented to facilitate Hymenoptera anatomy term discovery in literature. We present this tool, referred to as the 'Proofer', as part of an iterative approach to growing phenotype-relevant ontologies, regardless of domain. The process of ontology development results in a critical mass of terms that is applied as a filter to the source collection of articles in order to reveal term occurrence and biases in natural language species descriptions. Our results indicate that taxonomists use domain-specific terminology that follows taxonomic specialization, particularly at superfamily and family level groupings and that the developed Proofer tool is effective for term discovery, facilitating ontology construction.膜翅目（Hymenoptera）是包含锯蜂、蜂类、胡蜂以及蚂蚁的昆虫目，其表型多样性极高，自卡尔·林奈（Carolus Linnaeus）以来，已有超过14.5万种被记载于各类文本知识库中。然而，若缺乏往往耗时数十年的专业训练，这类文献往往难以解读。其中大量词汇属于领域专属术语（例如膜翅目生物学相关术语），此前尚未有全面的术语表，且存在大量同音异义、同义异形的表达。为解决这一难题，同时推动与膜翅目解剖学相关的后续学术交流，并助力领域专家积极参与解剖学本体（anatomy ontology）的开发工作，研究团队构建了膜翅目解剖学本体（Hymenoptera Anatomy Ontology, HAO）。作为HAO开发的一部分，研究团队实现了一款基于字典的主动学习自然语言识别工具，以助力从文献中挖掘膜翅目解剖学术语。我们将这款被命名为‘Proofer’的工具作为通用化迭代方法的一部分，用于拓展与表型相关的本体，无论其所属领域。本体开发过程会积累足够规模的术语集，将其作为过滤器应用于原始文献集合，以揭示自然语言物种描述中的术语出现情况与分布偏差。研究结果表明，分类学家所使用的领域专属术语遵循分类学专业化特征，尤其在总科与科级分类群中表现显著；同时所开发的Proofer工具在术语挖掘方面效果显著，可有效助力本体构建。