Ontology Matching Using Convolutional Neural Networks

Alexandre Bento, Amal Zouaq, Michel Gagnon


Abstract
In order to achieve interoperability of information in the context of the Semantic Web, it is necessary to find effective ways to align different ontologies. As the number of ontologies grows for a given domain, and as overlap between ontologies grows proportionally, it is becoming more and more crucial to develop accurate and reliable techniques to perform this task automatically. While traditional approaches to address this challenge are based on string metrics and structure analysis, in this paper we present a methodology to align ontologies automatically using machine learning techniques. Specifically, we use convolutional neural networks to perform string matching between class labels using character embeddings. We also rely on the set of superclasses to perform the best alignment. Our results show that we obtain state-of-the-art performance on ontologies from the Ontology Alignment Evaluation Initiative (OAEI). Our model also maintains good performance when tested on a different domain, which could lead to potential cross-domain applications.
Anthology ID:
2020.lrec-1.693
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5648–5653
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.693
DOI:
Bibkey:
Cite (ACL):
Alexandre Bento, Amal Zouaq, and Michel Gagnon. 2020. Ontology Matching Using Convolutional Neural Networks. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5648–5653, Marseille, France. European Language Resources Association.
Cite (Informal):
Ontology Matching Using Convolutional Neural Networks (Bento et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.693.pdf