Abstract

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.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	The final authenticated version will be available online - https://www.springer.com/gp/computer-science/lncs
Publisher Keywords:	Ontology Alignment; Semantic Embedding; Distant Supervision; Siamese Neural Network
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments:	School of Mathematics, Computer Science & Engineering > Computer Science
Date Deposited:	18 Mar 2021 13:45
URI:	https://openaccess.city.ac.uk/id/eprint/25810

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