Interactive ontology matching based on partial reference alignment

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • College of Information Science and Engineering, Fujian University of Technology
  • Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology
  • Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology

Abstract

The technique that enables the user and the automatic ontology matching tool to cooperate with each other to generate high-quality alignments in a reasonable amount of time is referred to as the interactive ontology matching. Interactive ontology matching poses a new challenge in a way of how to efficiently leverage user validation to improve the ontology alignment. To address this challenge, this paper presents an innovative interactive ontology matching technique based on Partial Reference Alignment (PRA) to better balance between the large workload posed on users and the demand of improving the quality of ontology alignment. In particular, a PRA-based Interactive Compact Hybrid Evolutionary Algorithm (ICHEA) is proposed to reduce user workload, by adaptively determining the timing of involving users, showing them the most problematic mappings, and helping them to deal with multiple conflicting mappings simultaneously. Meanwhile, it increases the value of user involvement by propagating the confidences of validated mappings, as well as reducing the negative effects brought by the erroneous user validations. The well-known OAEI 2016's benchmark track and interactive track are utilized to test the performance of this approach. The experimental results on benchmark track show that both the f-measure and the f-measure per second of this approach outperform those of the OAEI participants and three state-of-the-art Evolutionary Algorithm (EA) based ontology matching techniques. In addition, the experimental results of three interactive testing cases further show that ICHEA can efficiently determine high-quality ontology alignments under different cases of user error rates, and the performance of the approach is generally better than that of state-of-the-art interactive ontology matching systems.

Details

Original languageEnglish
Pages (from-to)355-370
JournalApplied Soft Computing
Volume72
Early online date13 Aug 2018
Publication statusPublished - 1 Nov 2018

Keywords

  • interactive ontology matching, partial ontology alignment, interactive compact hybrid evolutionary algorithm