  Ontology matching is a core task when creating interoperable and linked open
datasets. In this paper, we explore a novel structure-based mapping approach
which is based on knowledge graph embeddings: The ontologies to be matched are
embedded, and an approach known as absolute orientation is used to align the
two embedding spaces. Next to the approach, the paper presents a first,
preliminary evaluation using synthetic and real-world datasets. We find in
experiments with synthetic data, that the approach works very well on similarly
structured graphs; it handles alignment noise better than size and structural
differences in the ontologies.
