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Journal Articles Journal on Data Semantics Year : 2021

Measuring Clusters of Labels in an Embedding Space to Refine Relations in Ontology Alignment

Abstract

Ontology alignment plays a key role in the management of heterogeneous data sources and metadata. In this context, various ontology alignment techniques have been proposed to discover correspondences between the entities of different ontologies. This paper proposes a new ontology alignment approach based on a set of rules exploiting the embedding space and measuring clusters of labels to discover the relationship between entities. We tested our system on the OAEI conference complex alignment benchmark track and then applied it to aligning ontologies in a real-world case study. The experimental results show that the combination of word embedding and a measure of dispersion of the clusters of labels, which we call the radius measure, makes it possible to determine, with good accuracy, not only equivalence relations, but also hierarchical relations between entities.
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Dates and versions

hal-03403125 , version 1 (26-10-2021)

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Molka Tounsi Dhouib, Catherine Faron, Andrea G. B. Tettamanzi. Measuring Clusters of Labels in an Embedding Space to Refine Relations in Ontology Alignment. Journal on Data Semantics, 2021, ⟨10.1007/s13740-021-00137-8⟩. ⟨hal-03403125⟩
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