Utilizing descriptive statements from the biodiversity heritage library to expand the Hymenoptera Anatomy Ontology

Abstract

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.

Figure 1. Screenshot of the mx interface for string matching terms in the database with OCR text.

Possible additional new terms are proposed for the user to include.

Figure 2. Most commonly used anatomical terms in Hymenoptera.

Terms in this figure are ranked based on occurrence among all articles (how many articles a term occurred). Number on chart and size of pie represents the number of total times the term occurred in all articles.

Figure 3. The number of characters (terms) present in at least 2, 10, 50, and 100 articles.

Figure 4. Variation of number of returned clusters based on clustering method and term occurrence in articles.

Figure 5. Sorensen Average tree with superfamily name, and number of groupings calculated to superfamily level.

The tree represented is the entire, untrimmed tree and the number after the superfamily is the number of groupings retrieved when the tree is trimmed.