  One of the strongest signals for automated matching of ontologies and
knowledge graphs are the textual descriptions of the concepts. The methods that
are typically applied (such as character- or token-based comparisons) are
relatively simple, and therefore do not capture the actual meaning of the
texts. With the rise of transformer-based language models, text comparison
based on meaning (rather than lexical features) is possible. In this paper, we
model the ontology matching task as classification problem and present
approaches based on transformer models. We further provide an easy to use
implementation in the MELT framework which is suited for ontology and knowledge
graph matching. We show that a transformer-based filter helps to choose the
correct correspondences given a high-recall alignment and already achieves a
good result with simple alignment post-processing methods.
