Automatic Configuration Selection Using Ontology Matching Task Profiling

Abstract

An ontology matching system can usually be run with different configurations that optimize the systems effectiveness, namely precision, recall, or F-measure, depending on the specific ontologies to be aligned. Changing the configuration has potentially high impact on the obtained results. We apply matching task profiling metrics to automatically optimize the systems configuration depending on the characteristics of the ontologies to be matched. Using machine learning techniques, we can automatically determine the optimal configuration in most cases. Even using a small training set, our system determines the best configuration in 94 of the cases. Our approach is evaluated using the AgreementMaker ontology matching system, which is extensible and configurable.

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Document Details

Document Type
Technical Report
Publication Date
May 27, 2012
Accession Number
AD1187712

Entities

People

  • Alessio Fabiani
  • Cosmin Stroe
  • Federico Caimi
  • Isabel F. Cruz
  • Matteo Palmonari

Organizations

  • University of Illinois at Chicago
  • University of Milan-Bicocca

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Automatic
  • Computer Languages
  • Computer Science
  • Data Mining
  • Engineering
  • Machine Learning
  • Neural Networks
  • Ontologies
  • Precision
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Computer Vision.
  • Database Systems and Applications

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval