Skip to main content

Research Repository

Advanced Search

Ontology alignment based on word embedding and random forest classification.

Nkisi-Orji, Ikechukwu; Wiratunga, Nirmalie; Massie, Stewart; Hui, Kit-Ying; Heaven, Rachel

Authors

Kit-Ying Hui

Rachel Heaven



Contributors

Michele Berlingerio
Editor

Francesco Bonchi
Editor

Thomas Gärtner
Editor

Neil Hurley
Editor

Georgiana Ifrim
Editor

Abstract

Ontology alignment is crucial for integrating heterogeneous data sources and forms an important component for realising the goals of the semantic web. Accordingly, several ontology alignment techniques have been proposed and used for discovering correspondences between the concepts (or entities) of different ontologies. However, these techniques mostly depend on string-based similarities which are unable to handle the vocabulary mismatch problem. Also, determining which similarity measures to use and how to effectively combine them in alignment systems are challenges that have persisted in this area. In this work, we introduce a random forest classifier approach for ontology alignment which relies on word embedding to discover semantic similarities between concepts. Specifically, we combine string-based and semantic similarity measures to form feature vectors that are used by the classifier model to determine when concepts match. By harnessing background knowledge and relying on minimal information from the ontologies, our approach can deal with knowledge-light ontological resources. It also eliminates the need for learning the aggregation weights of multiple similarity measures. Our experiments using Ontology Alignment Evaluation Initiative (OAEI) dataset and real-world ontologies highlight the utility of our approach and show that it can outperform state-of-the-art alignment systems.

Citation

NKISI-ORJI, I., WIRATUNGA, N., MASSIE, S., HUI, K.-Y. and HEAVEN, R. 2019. Ontology alignment based on word embedding and random forest classification. In Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N. and Ifrim, G. (eds.) Machine learning and knowledge discovery in databases: proceedings of the 2018 European conference on machine learning and principles and practice of knowledge discovery in databases (ECML PKDD 2018), 10-14 September 2018, Dublin, Ireland. Lecture notes in computer science, 11051. Cham: Springer [online], part I, pages 557-572. Available from: https://doi.org/10.1007/978-3-030-10925-7_34

Conference Name 2018 European conference on machine learning and principles and practice of knowledge discovery in databases (ECML PKDD 2018)
Conference Location Dublin, Ireland
Start Date Sep 10, 2018
End Date Sep 14, 2018
Acceptance Date Jun 14, 2018
Online Publication Date Jan 18, 2019
Publication Date Feb 15, 2019
Deposit Date Jun 26, 2018
Publicly Available Date Jan 18, 2019
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer
Pages 557-572
Series Title Lecture notes in computer science
Series Number 11051
Series ISSN 1611-3349
Book Title Machine learning and knowledge discovery in databases
ISBN 9783030109240
DOI https://doi.org/10.1007/978-3-030-10925-7_34
Keywords Ontology alignment; Word embedding; Machine classification; Semantic web
Public URL http://hdl.handle.net/10059/2968

Files





You might also like



Downloadable Citations