ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2018
Extended Analysis of Topological-Pattern-Based Ontology Enrichment.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Alternative classification of identical concepts in different terminologies: Different ways to view the world.Journal of biomedical informatics · 2019Trial
- Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement.Journal of biomedical informatics · 2025Article
- Extending import detection algorithms for concept import from two to three biomedical terminologies.BMC medical informatics and decision making · 2020Article
- A review of auditing techniques for the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches.Scientific reports · 2020Article
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Authors and funding
4 authors.
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Abstract
Maintenance of biomedical ontologies is difficult. We have previously developed a topological-pattern-based method to deal with the problem of identifying concepts in a reference ontology that could be of interest for insertion into a target ontology. Assuming that both ontologies are parts of the Unified Medical Language System (UMLS), the method suggests approximate locations where the target ontology could be extended with new concepts from the reference ontology. However, the final decision about each concept has to be made by a human expert. In this paper, we describe the universe of cross-ontology topological patterns in quantitative terms. We then present a theoretical analysis of the number of potential placements of reference concepts in a path in a target ontology, allowing for new cross-ontology synonyms. This provides a rough estimate of what expert resources need to be allocated for the task. One insight in previous work on this topic was the large percentage of cases where importing concepts was impossible, due to a configuration called "alternative classification." In this paper, we confirm this observation. Our target ontology is the National Cancer Institute thesaurus (NCIt). However, the methods can be applied to other pairs of ontologies with hierarchical relationships from the UMLS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.