Ontology Matching using Textual Class Descriptions
Résumé
In this paper, we propose TEXTO, a TEXT-based Ontology matching system. This matcher
leverages the rich semantic information of classes available in most ontologies by a combination
of a pre-trained word embedding model and a pre-trained language model. Its performance is
evaluated on the datasets of the OAEI Common Knowledge Graphs Track, augmented with the
description of each class, and a new dataset based on the refreshed alignment of Schema.org
and Wikidata. Our results demonstrate that TEXTO outperforms all state-of-art matchers
in terms of precision, recall, and F1 score. In particular, we show that almost perfect class
alignment can be achieved using textual content only, excluding any structural information
like the graph of classes or the instances of each class.
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ISWC-OM-2023/main.pdf (460.4 Ko)
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ISWC-OM-2023/cc-by.pdf (58.48 Ko)
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ISWC-OM-2023/main.synctex.gz (71.41 Ko)
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