Abstract
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.
Yiwen Peng
Ph.D. Student in Knowledge Graphs
My research focuses on knowledge graphs, with a particular focus on knowledge integration and completion using language models.