Representing dental restoration materials in the oral health and disease ontology

Semantic clarity and standardization in representing dental restoration materials are essential for ensuring interoperability across research and clinical settings. However, existing ontologies, including those in the Open Biological and Biomedical Ontology (OBO) Foundry, provide minimal coverage of this clinically relevant domain. Guided by OBO Foundry principles, developed an ontological extension of the Oral Health and Disease (OHD) ontology to formally represent dental restoration materials. The modeling process included iterative expert input to ensure clinical accuracy and precise terminology. Logical definitions and subclass hierarchies were created to classify materials by composition and microstructure. The extended ontology introduces a logically structured taxonomy of dental restoration materials using genus-differentia definitions. Its class hierarchy aligns with domain-specific classification systems and captures current and emerging material types. Reuse of class from the Chemical Entities of Biological Interest (ChEBI) and the Environment Ontology (ENVO) as well as relations from the Relation Ontology (RO) supports semantic integration with related biomedical ontologies. This work highlights how domain-informed ontology design can effectively capture complex material knowledge. The extensive collaboration integrates expertise from ontology engineers, academic researchers, and practicing clinicians. This approach enables the capture of intricate, often-disregarded real clinical factors, resulting in a model that reflects both the structural properties of materials and their practical use. The extended OHD ontology improves the semantic representation of dental restoration materials and provides a scalable foundation for advancing oral health informatics.

Authors

Institutions

Publication Details

Journal
Journal of Biomedical Semantics
Published
2026-04-16
DOI
https://doi.org/10.1186/s13326-026-00352-x
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Representing dental restoration materials in the oral health and disease ontology

William D. Duncan, Nivedita Dutta, Michael DeBellis, Alexander D. Diehl et al.
Journal of Biomedical Semantics
Biomedical Text Mining and Ontologies
article

Representing dental restoration materials in the oral health and disease ontology

William D. Duncan, Nivedita Dutta, Michael DeBellis, Alexander D. Diehl, Nripen Chanda, Gopikrishnan M. Chandrasekharan, Finn Wilson, M Rocha, Mattew Diller
article en

Abstract

Semantic clarity and standardization in representing dental restoration materials are essential for ensuring interoperability across research and clinical settings. However, existing ontologies, including those in the Open Biological and Biomedical Ontology (OBO) Foundry, provide minimal coverage of this clinically relevant domain. Guided by OBO Foundry principles, developed an ontological extension of the Oral Health and Disease (OHD) ontology to formally represent dental restoration materials. The modeling process included iterative expert input to ensure clinical accuracy and precise terminology. Logical definitions and subclass hierarchies were created to classify materials by composition and microstructure. The extended ontology introduces a logically structured taxonomy of dental restoration materials using genus-differentia definitions. Its class hierarchy aligns with domain-specific classification systems and captures current and emerging material types. Reuse of class from the Chemical Entities of Biological Interest (ChEBI) and the Environment Ontology (ENVO) as well as relations from the Relation Ontology (RO) supports semantic integration with related biomedical ontologies. This work highlights how domain-informed ontology design can effectively capture complex material knowledge. The extensive collaboration integrates expertise from ontology engineers, academic researchers, and practicing clinicians. This approach enables the capture of intricate, often-disregarded real clinical factors, resulting in a model that reflects both the structural properties of materials and their practical use. The extended OHD ontology improves the semantic representation of dental restoration materials and provides a scalable foundation for advancing oral health informatics.

Journal of Biomedical SemanticsVol. 17(1)
National Institutes of Health (US), National Institute of Technology Durgapur (IN), Sobell House (GB), United States National Library of Medicine (US), University of Florida (US), Indiana University – Purdue University Indianapolis (US), University at Buffalo, State University of New York (US), Central Mechanical Engineering Research Institute (IN), Florida College (US)
Openalex Percentile: Top 21%
Biomedical Text Mining and Ontologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.