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Updated: Apr 14, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems.

Julio Cesar Dos Reis1, Cédric Pruski2, Marcos Da Silveira2

  • 1Institute of Computing, University of Campinas, Av. Albert Einstein, 1251, Cidade Universitária Zeferino Vaz, 13083-852 Campinas, SP, Brazil.

Journal of Biomedical Informatics
|April 19, 2015
PubMed
Summary

Maintaining up-to-date knowledge organization system (KOS) mappings is crucial. This study introduces a framework using formal heuristics to automatically adapt KOS mappings during knowledge evolution, improving data quality.

Keywords:
Biomedical ontologiesMapping adaptationMapping evolutionMapping maintenanceOntology alignmentOntology evolution

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Information Science

Background:

  • Knowledge Organization Systems (KOS) and their mappings are vital for decision support systems.
  • Knowledge evolution can invalidate existing KOS mappings, necessitating maintenance.
  • Semi-automatic methods are required to keep semantic correspondences current.

Purpose of the Study:

  • To develop and evaluate a framework for adapting KOS mappings in response to KOS evolution.
  • To address the challenge of maintaining the accuracy and validity of semantic correspondences over time.

Main Methods:

  • A novel framework employing formal heuristics to drive the adaptation of KOS mappings was developed.
  • The framework considers established mappings, KOS evolution, and potential mapping changes.
  • Experimental evaluation was conducted on biomedical KOS mappings.

Main Results:

  • The proposed framework demonstrated effective adaptation of mappings affected by KOS evolution.
  • High performance in precision, recall, and F-measure was observed across diverse biomedical datasets.
  • The approach significantly improves the quality and timeliness of KOS mappings.

Conclusions:

  • The developed framework facilitates largely automatic adaptation of KOS mappings, simplifying maintenance.
  • Implemented algorithms and tools reduce user effort in KOS mapping maintenance.
  • The study contributes to more robust and reliable knowledge organization systems.