Tensor-Based Syntactic Feature Engineering for Ontology Instance Matching

Andrzej Szwabe; Paweł Misiorek; Jarosław Bąk; Michał Ciesielczyk

doi:10.1007/978-3-319-59060-8_55

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Title

Tensor-Based Syntactic Feature Engineering for Ontology Instance Matching

Authors

Andrzej Szwabe (WE) ^{[ 1 ][ 2.3 ][ P ]}
Paweł Misiorek (WE) ^{[ 1 ][ 2.3 ][ P ]}
Jarosław Bąk (WE) ^{[ 1 ][ P ]}
Michał Ciesielczyk (WE) ^{[ 1 ][ 2.3 ][ P ]}

^{[ 1 ]} Instytut Automatyki, Robotyki i Inżynierii Informatycznej, Wydział Elektryczny, Politechnika Poznańska | ^{[ P ]} employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2017

Chapter type

chapter in monograph / paper

Publication language

english

Keywords

EN

OAEI
ontology instance matching
machine learning
tensor-based data modeling
Natural Language Processing
syntactic analysis

Abstract

EN 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.

Date of online publication

24.05.2017

Pages (from - to)

609 - 622

DOI

10.1007/978-3-319-59060-8_55

URL

https://link.springer.com/chapter/10.1007/978-3-319-59060-8_55

Book

Artificial Intelligence and Soft Computing : 16th International Conference, ICAISC 2017, Zakopane, Poland, June 11-15, 2017 : Proceedings : Part II

Presented on

16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, 11-15.06.2017, Zakopane, Poland