ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010
Identifying Granularity Differences between Large Biomedical Ontologies through Rules.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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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
- Extending import detection algorithms for concept import from two to three biomedical terminologies.BMC medical informatics and decision making · 2020Article
- Extended Analysis of Topological-Pattern-Based Ontology Enrichment.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018Article
- Leveraging Horizontal Density Differences between Ontologies to Identify Missing Child Concepts: A Proof of Concept.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- A comparative analysis of the density of the SNOMED CT conceptual content for semantic harmonization.Artificial intelligence in medicine · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study is to identify the granularity differences as well as similarity between large biomedical ontologies through rules. Two anatomical ontologies were selected, and based on a set of concept mappings obtained through simple string matching techniques, we constructed rules to distinguish among different types of subclasses and classifications. 82% of the concept mappings have exactly the same classification in subclasses between the two ontologies. Other mappings are classified in different granularity, including additional subclasses, detailed classification, and different intermediate classification concepts. Using rules and the rule inference engine enables an automatic and scalable investigation of the structural incompatibility among biomedical ontologies.
Indexed as
Identifiers
21347114PMC3041335What OpenQuestion holds
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.