Tensor-based syntactic feature engineering for ontology instance matching

Abstract

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.