ArticleBMC cardiovascular disorders2025
Semantic web-based ontology: a comprehensive framework for cardiovascular knowledge representation.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Anatomy-Guided Hybrid CNN-ViT Model with Neuro-Symbolic Reasoning for Early Diagnosis of Thoracic Diseases Multilabel.Diagnostics (Basel, Switzerland) · 2026Article
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7 authors.
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Abstract
In the healthcare industry, the Semantic Web offers to manage a huge amount of medical data which is machine-readable and machine-understandable as well. This domain incorporates ontologies, linked data, and semantic web technologies to promote healthcare data interoperability and facilitate the effective and precise exchange of medical expertise, medical records, clinical recommendations, and research. It is tracked down that knowledge is better comprehended after undergoing an ontological analysis. An ontology serves as the basis of any knowledge representation system for a certain domain and eliminates inconsistencies in data to ensure its validity. The necessity of establishing healthcare systems for heart diseases is emphasized by the significant lack of awareness among the general public. A heart disease ontology needed to be created as it is not as thoroughly considered and explored (like, the classes of main heart diseases have not been structured) as it should have. In this research, we intend to develop a comprehensive ontology i.e. Heart Disease Ontology (HDO) that serves as a knowledge gateway for knowledge concerning major heart diseases. This ontology provides precise, comprehensive, and reliable data on prevalent heart diseases, including their causes, risk factors, symptoms, diagnosis, and treatment. HDO represents an extensive ontology structure consisting of 104 classes, 20 object properties, 14 data properties, and 808 instances. To ensure the interoperability of HDO, we have utilized some schema classes and properties and also reused classes from existing medical standardized ontologies. From the metrics of HDO, 22 classes, 5 object properties, and 12 data properties were reused from schema.org. Other than that, we have incorporated international medical standards by utilizing 18 classes from existing medical ontologies including, SNOMED CT, ICD10, FHIR, NCIT, SCDO, and some others. HDO is evaluated using OOPs Pitfall Scanner and Hermit Reasoner. Its functionality is validated by populating a use case and executing SPARQL queries. The domain knowledge and use case of HDO was validated through a domain expert study conducted in association with a cardiologist. HDO is developed by integrating Ontology Web Language (OWL) and RDF (Resource Description Framework) within the Protégé environment.
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