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UVA Resources for the Biomedical Vocabulary Alignment at Scale in the UMLS Metathesaurus

arXiv 2205.10575 ↗First posted 21 May 2022cs.AIcited by 0

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Abstract (arXiv)

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).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 46 references that are in the map. First posted on arXiv 21 May 2022.

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References · 17

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  11. 2020A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphscited by 120
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5 more
  1. 2018Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignmentcited by 49
  2. 2018Ontology Alignment in the Biomedical Domain Using Entity Definitions and Contextcited by 10
  3. 2018Knowledge Representation Learning: A Quantitative Reviewcited by 9
  4. 2021Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesauruscited by 2
  5. 2022UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesauruscited by 1

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