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Updated: Jan 24, 2026

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Ontology mapping for semantically enabled applications.

Ian Harrow1, Rama Balakrishnan2, Ernesto Jimenez-Ruiz3

  • 1Ian Harrow Consulting Ltd, Whitstable, UK.

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Summary
This summary is machine-generated.

Ontology mapping (OM) is crucial for leveraging big biomedical data. This review covers OM challenges, solutions, tools, and the development of sustainable ontology mapping services (OMS) for advanced research.

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Knowledge Representation

Background:

  • Biomedical research generates vast, diverse data, necessitating advanced methods for data integration and analysis.
  • Realizing the full potential of semantically enriched applications and machine learning in areas like drug discovery requires robust data interoperability.

Purpose of the Study:

  • To review recent advancements in ontology mapping (OM).
  • To discuss challenges, solutions, and selection criteria for ontologies.
  • To outline requirements and progress for sustainable ontology mapping services (OMS).

Main Methods:

  • Literature review of recent progress in ontology mapping.
  • Discussion of challenges, solutions, selection strategies, and evaluation metrics for OM.
  • Analysis of requirements and development status of ontology mapping services.

Main Results:

  • Identified key challenges and proposed solutions in current ontology mapping practices.
  • Described various tools and algorithms for ontology mapping, including their evaluation.
  • Outlined essential components and progress towards implementing sustainable ontology mapping services.

Conclusions:

  • Effective ontology mapping is vital for unlocking insights from big biomedical data.
  • Development of comprehensive ontology mapping services is progressing to support data-driven research and applications.