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Related Experiment Video

Updated: May 21, 2026

Finite Element Analysis Model for Assessing Expansion Patterns from Surgically Assisted Rapid Palatal Expansion

Finite Element Analysis Model for Assessing Expansion Patterns from Surgically Assisted Rapid Palatal Expansion

Published on: October 20, 2023

Extending ontologies by finding siblings using set expansion techniques.

Götz Fabian¹, Thomas Wächter, Michael Schroeder

¹Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062 Dresden, Germany. goetz.fabian@biotec.tu-dresden.de

Bioinformatics (Oxford, England)

|June 13, 2012

Summary

This summary is machine-generated.

This study introduces a novel method for automatically extending biomedical ontologies by discovering new sibling terms from the web. The approach enhances ontology quality and maturity, as demonstrated by its successful application to Medical Subject Headings (MeSH).

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Last Updated: May 21, 2026

Finite Element Analysis Model for Assessing Expansion Patterns from Surgically Assisted Rapid Palatal Expansion

Finite Element Analysis Model for Assessing Expansion Patterns from Surgically Assisted Rapid Palatal Expansion

Published on: October 20, 2023

Area of Science:

Biomedical informatics
Ontology engineering
Knowledge representation

Background:

Biomedical ontologies are crucial for knowledge representation but often lack comprehensive coverage and maturity.
Automated methods are needed to systematically enhance the quality and completeness of existing ontologies.

Purpose of the Study:

To develop and evaluate an automated approach for extending ontologies by discovering new sibling terms.
To assess the completeness of ontologies, using Medical Subject Headings (MeSH) as a case study.

Main Methods:

Combined two web-based approaches for retrieving new terms: HTML structure exploitation and text mining.
Focused on discovering terms in a sibling relationship to existing ontology terms.
Integrated the sibling discovery functionality into DOG4DAG, an ontology generation plugin for Protégé and OBO-Edit.

Main Results:

The developed method successfully suggests high-quality ontology terms, achieving 80% recall and 61% precision in evaluations against MeSH.
The two independent approaches demonstrated complementary strengths in sibling discovery.
The evaluation indicated that MeSH is largely complete within its medical domain.

Conclusions:

The automated sibling discovery approach is effective for improving ontology quality and assessing completeness.
DOG4DAG is the first ontology generation plugin to offer on-the-fly sibling discovery.
The tool is available for Protégé and OBO-Edit, facilitating practical application in ontology engineering.