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An Alignment-Based Implementation of a Holistic Ontology Integration Method.

Inès Osman1, Salvatore Flavio Pileggi2, Sadok Ben Yahia1,3

  • 1LIPAH - LR11ES14, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis 2092, Tunisia.

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Summary
This summary is machine-generated.

This study introduces a holistic method for integrating multiple ontologies simultaneously, reducing integration costs. The approach preserves original knowledge and offers refactoring options for customized ontology integration.

Keywords:
00-0199-00Bridge ontologyFull-Merge ontologyOntologyOntology integrationOntology mergingOntology refactoringSemantic interoperability

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Ontology integration faces challenges due to increasing ontology scale, complexity, and heterogeneity in systems like the Semantic Web.
  • Existing methods often use incremental pairwise integration, which may not scale efficiently for numerous ontologies.

Purpose of the Study:

  • To present a holistic, alignment-based method for customized ontology integration.
  • To address the challenges of integrating multiple ontologies concurrently, preserving original knowledge.

Main Methods:

  • A holistic approach integrating multiple ontologies at once, using established ontology matching techniques.
  • The method accepts multiple ontologies and pairwise alignments as input.
  • Two implementations, OIAR (bridge ontology) and AROM (merged ontology), were developed, including an ontology refactoring option.

Main Results:

  • The proposed method effectively integrates large biomedical ontologies.
  • Demonstrated a decreased integration cost when handling multiple ontologies compared to pairwise approaches.
  • The resulting integrated ontology faithfully preserves the knowledge from the input ontologies and alignments.

Conclusions:

  • The holistic, alignment-based method offers an effective solution for customized ontology integration.
  • This approach reduces integration costs and preserves knowledge integrity, particularly for large-scale biomedical datasets.
  • OIAR and AROM provide flexible implementations for ontology integration, with or without refactoring.