  The construction and maintenance process of the UMLS (Unified Medical
Language System) Metathesaurus is time-consuming, costly, and error-prone as it
relies on (1) the lexical and semantic processing for suggesting synonymous
terms, and (2) the expertise of UMLS editors for curating the suggestions. For
improving the UMLS Metathesaurus construction process, our research group has
defined a new task called UVA (UMLS Vocabulary Alignment) and generated a
dataset for evaluating the task. Our group has also developed different
baselines for this task using logical rules (RBA), and neural networks (LexLM
and ConLM).
  In this paper, we present a set of reusable and reproducible resources
including (1) a dataset generator, (2) three datasets generated by using the
generator, and (3) three baseline approaches. We describe the UVA dataset
generator and its implementation generalized for any given UMLS release. We
demonstrate the use of the dataset generator by generating datasets
corresponding to three UMLS releases, 2020AA, 2021AA, and 2021AB. We provide
three UVA baselines using the three existing approaches (LexLM, ConLM, and
RBA). The code, the datasets, and the experiments are publicly available,
reusable, and reproducible with any UMLS release (a no-cost license agreement
is required for downloading the UMLS).

Artificial Intelligence