Augmenting ontology alignment by semantic embedding and distant supervision

Conference item

Ontology alignment plays a critical role in knowledge integration and has been widely investigated in the past decades. State of the art systems, however, still have considerable room for performance improvement especially in dealing with new (industrial) alignment tasks. In this paper we present a machine learning based extension to traditional ontology alignment systems, using distant supervision for training, ontology embedding and Siamese Neural Networks for incorporating richer semantics. We have used the extension together with traditional systems such as LogMap and AML to align two food ontologies, HeLiS and FoodOn, and we found that the extension recalls many additional valid mappings and also avoids some false positive mappings. This is also verified by an evaluation on alignment tasks from the OAEI conference track.

Authors: Chen, J; Jiménez-Ruiz, E; Horrocks, I; Antonyrajah, D; Hadian, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Host title: The Semantic Web. ESWC 2021
• Pages: 392-408
• Series: Lecture Notes in Computer Science
• Series number: 12731
• Place of publication: Cham, Switzerland
• Publication date: 2021-05-31
• Event title: 18th International Conference, ESWC 2021
• Event location: Virtual Event
• Event website: https://2021.eswc-conferences.org/
• Event start date: 2021-06-06
• Event end date: 2021-06-10
• DOI: 10.1007/978-3-030-77385-4_23
• EISSN: 1611-3349
• ISSN: 0302-9743
• EISBN: 9783030773854
• ISBN: 9783030773847
• Language: English
• Keywords: Siamese neural network; semantic embedding; FFR; distant supervision; ontology alignment