Abstract
Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based map** 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.
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Notes
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Variations of this formulations are possible, e.g., including different dimensions for the vector spaces of E and R, and/or using complex instead of real numbers.
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The Ontology Alignment Evaluation Initiative (OAEI) provides reference alignments and carries out yearly evaluation campaigns since 2004. For more information, see http://oaei.ontologymatching.org/.
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Portisch, J., Costa, G., Stefani, K., Kreplin, K., Hladik, M., Paulheim, H. (2022). Ontology Matching Through Absolute Orientation of Embedding Spaces. In: Groth, P., et al. The Semantic Web: ESWC 2022 Satellite Events. ESWC 2022. Lecture Notes in Computer Science, vol 13384. Springer, Cham. https://doi.org/10.1007/978-3-031-11609-4_29
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