A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System

Abstract

Objective The Unified Medical Language System (UMLS) integrates various source terminologies to support interoperability between biomedical information systems. In this article, we introduce a novel transformation-based auditing method that leverages the UMLS knowledge to systematically identify missing hierarchical IS-A relations in the source terminologies. Materials and Methods Given a concept name in the UMLS, we first identify its base and secondary noun chunks. For each identified noun chunk, we generate replacement candidates that are more general than the noun chunk. Then, we replace the noun chunks with their replacement candidates to generate new potential concept names that may serve as supertypes of the original concept. If a newly generated name is an existing concept name in the same source terminology with the original concept, then a potentially missing IS-A relation between the original and the new concept is identified. Results Applying our transformation-based method to English-language concept names in the UMLS (2019AB release), a total of 39 359 potentially missing IS-A relations were detected in 13 source terminologies. Domain experts evaluated a random sample of 200 potentially missing IS-A relations identified in the SNOMED CT (U.S. edition) and 100 in Gene Ontology. A total of 173 of 200 and 63 of 100 potentially missing IS-A relations were confirmed by domain experts, indicating that our method achieved a precision of 86.5% and 63% for the SNOMED CT and Gene Ontology, respectively. Conclusions Our results showed that our transformation-based method is effective in identifying missing IS-A relations in the UMLS source terminologies.

Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

“…The section editors achieved a first selection of 226 papers based on the title and abstract. After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established [ 5 - 19 ]. Five reviewers reviewed these pre-selected papers to select the best four final papers [ 5 - 8 ].…”

“…After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established [ 5 - 19 ]. Five reviewers reviewed these pre-selected papers to select the best four final papers [ 5 - 8 ]. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design, and the usual KRM application domains: bioinformatics, machine learning, and electronic health records (EHRs).…”

“…Among the best papers in 2020, ontology curation research is in the spotlight. Zheng uses the Unified Medical Language System (UMLS) to discover missing is-a relations in standard terminologies [ 5 ]. They developed a transformation-based method which uses the rich knowledge provided by the UMLS for auditing and improving the qualities of its source terminologies.…”

“…The final step of the selection of papers was achieved during a meeting of the whole editorial board, based on the reviews and the report of the section editors. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Five reviewers reviewed these pre-selected papers to select the best four final papers [5][6][7][8].…”

“…[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Five reviewers reviewed these pre-selected papers to select the best four final papers [5][6][7][8]. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design, and the usual KRM application domains: bioinformatics, machine learning, and electronic health records (EHRs).…”