  One of the strongest signals for automated matching of knowledge graphs and
ontologies are textual concept descriptions. With the rise of transformer-based
language models, text comparison based on meaning (rather than lexical
features) is available to researchers. However, performing pairwise comparisons
of all textual descriptions of concepts in two knowledge graphs is expensive
and scales quadratically (or even worse if concepts have more than one
description). To overcome this problem, we follow a two-step approach: we first
generate matching candidates using a pre-trained sentence transformer (so
called bi-encoder). In a second step, we use fine-tuned transformer
cross-encoders to generate the best candidates. We evaluate our approach on
multiple datasets and show that it is feasible and produces competitive
results.
