Tensor-Based Syntactic Feature Engineering for Ontology Instance Matching

  • Andrzej Szwabe
  • Paweł Misiorek
  • Jarosław Bąk
  • Michał Ciesielczyk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10246)


We investigate a machine learning approach to ontology instance matching. We apply syntactic and lexical text analysis as well as tensor-based data representation as means for feature engineering effectively supporting supervised learning based on logistic regression. We experimentally evaluate our approach in the scenario of the SABINE Data linking subtask defined by Ontology Alignment Evaluation Initiative. We show that, as far as the prediction of non-trivial matches is concerned, the use of the proposed tensor-based modelling of lexical and syntactical properties of the ontology instances enables achieving a significant quality improvement.


OAEI Ontology instance matching Machine learning Tensor-based data modeling Natural Language Processing Syntactic analysis 



This work is supported by the Polish National Science Centre, grant DEC-2011/01/D/ST6/06788.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrzej Szwabe
    • 1
  • Paweł Misiorek
    • 1
  • Jarosław Bąk
    • 1
  • Michał Ciesielczyk
    • 1
  1. 1.Institute of Control and Information EngineeringPoznan University of TechnologyPoznanPoland

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