Ontology Matching Through Absolute Orientation of Embedding Spaces
This addresses interoperability for linked open datasets, but it is incremental as it builds on existing embedding and alignment techniques.
The paper tackles ontology matching by aligning embedding spaces of knowledge graphs using absolute orientation, achieving strong performance on similarly structured synthetic graphs and better handling alignment noise than structural differences.
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.