Ontology Matching Through Absolute Orientation of Embedding Spaces

• URL: http://arxiv.org/abs/2204.04040v1
• Date: Fri, 8 Apr 2022 12:59:31 GMT
• Title: Ontology Matching Through Absolute Orientation of Embedding Spaces
• Authors: Jan Portisch, Guilherme Costa, Karolin Stefani, Katharina Kreplin, Michael Hladik, Heiko Paulheim
• Abstract summary: Ontology is a core task when creating interoperable and linked open datasets. In this paper, we explore a structure-based mapping approach which is based on knowledge graph embeddings. We find in experiments with synthetic data, that the approach works very well on similarly structured datasets.
• Score: 1.5169370091868053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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