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Daniel Faria1, Catia Pesquita2, Emanuel Santos3

  • 1LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal.

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|November 8, 2014
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Summary
This summary is machine-generated.

This study introduces an automated method to select background knowledge for ontology matching, improving accuracy in complex domains like life sciences. The novel "mapping gain" metric effectively identifies the most beneficial knowledge sources for better semantic web integration.

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Information Science

Background:

  • Ontology matching is crucial for the Semantic Web, especially in complex life science domains.
  • Effective use of background knowledge significantly enhances ontology matching success.
  • Current systems often require manual selection or predefinition of background knowledge sources.

Purpose of the Study:

  • To propose a novel methodology for the automatic selection of background knowledge sources in ontology matching.
  • To address the limitations of predefined or user-provided background knowledge in existing systems.

Main Methods:

  • Developed a methodology to automatically select background knowledge sources for ontology matching.
  • Introduced the 'mapping gain' metric to assess the usefulness of background knowledge sources.
  • Implemented the methodology in the AgreementMakerLight framework.
  • Evaluated the approach using benchmark biomedical ontology matching tasks from OAEI 2013.

Main Results:

  • The methodology consistently identified optimal background knowledge sources across various matching problems.
  • The selected sources led to significant improvements compared to baseline alignments (without background knowledge).
  • The 'mapping gain' parameter showed a strong correlation with the F-measure of the resulting alignments.

Conclusions:

  • The proposed methodology effectively automates the selection of background knowledge for ontology matching.
  • The 'mapping gain' metric serves as a reliable estimator for background knowledge-based ontology matching techniques.
  • This approach enhances the efficiency and effectiveness of semantic web applications, particularly in specialized domains.