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The Impact of Imbalanced Training Data on Local Matching Learning of Ontologies

  • Amir LaadharEmail author
  • Faiza Ghozzi
  • Imen Megdiche
  • Franck Ravat
  • Olivier Teste
  • Faiez Gargouri
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 353)

Abstract

Matching learning corresponds to the combination of ontology matching and machine learning techniques. This strategy has gained increasing attention in recent years. However, state-of-the-art approaches implementing matching learning strategies are not well-tailored to deal with imbalanced training sets. In this paper, we address the problem of the imbalanced training sets and their impacts on the performance of the matching learning in the context of aligning biomedical ontologies. Our approach is applied to local matching learning, which is a technique used to divide a large ontology matching task into a set of distinct local sub-matching tasks. A local matching task is based on a local classifier built using its balanced local training set. Thus, local classifiers discover the alignment of the local sub-matching tasks. To validate our approach, we propose an experimental study to analyze the impact of applying conventional resampling techniques on the quality of the local matching learning.

Keywords

Imbalanced training data Machine learning Ontology matching Semantic web 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Amir Laadhar
    • 1
    Email author
  • Faiza Ghozzi
    • 2
  • Imen Megdiche
    • 1
  • Franck Ravat
    • 1
  • Olivier Teste
    • 1
  • Faiez Gargouri
    • 2
  1. 1.Institut de Recherche en Informatique de ToulouseToulouseFrance
  2. 2.MIRACLSfax UniversitySfaxTunisia

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