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Related Experiment Video

Updated: Jun 28, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Auditing the NCI thesaurus with semantic web technologies.

Fleur Mougin¹, Olivier Bodenreider

¹LESIM, INSERM U897, ISPED, University Victor Segalen Bordeaux 2, France. fleur.mougin@isped.u-bordeaux2.fr

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|November 13, 2008

Summary

This study used Semantic Web technologies to audit the National Cancer Institute (NCI) thesaurus, finding inconsistencies in its hierarchical relations. The method improved biomedical terminology quality by identifying and discussing these data quality issues.

Related Experiment Videos

Last Updated: Jun 28, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Biomedical Informatics
Semantic Web Technologies
Data Quality Assessment

Background:

Biomedical terminologies require consistent auditing to ensure data quality.
Existing methods for auditing may not fully capture complex relationships within terminologies.

Purpose of the Study:

To present a Semantic Web-based method for auditing biomedical terminologies.
To apply this method to the National Cancer Institute (NCI) thesaurus.
To assess the consistency of hierarchical and associative relations within the NCI thesaurus and against the Unified Medical Language System (UMLS) Semantic Network.

Main Methods:

Storing the NCI thesaurus in a Resource Description Framework (RDF) triple store.
Querying the RDF triple store to analyze concept properties and relations.
Comparing NCI thesaurus relations with those in the UMLS Semantic Network.

Main Results:

The Semantic Web approach successfully identified inconsistencies in the NCI thesaurus.
Associative relations in the NCI thesaurus demonstrated higher consistency than hierarchical relations.
The study highlighted specific causes of inconsistency within the terminology.

Conclusions:

Semantic Web technologies offer a robust framework for auditing biomedical terminologies.
The auditing process revealed areas for improving the quality and consistency of the NCI thesaurus.
This method provides benefits for maintaining high-quality biomedical data standards.