Background Knowledge in Schema Matching: Strategy vs. Data. (arXiv:2107.00001v1 [cs.DB])

The use of external background knowledge can be beneficial for the task of
matching schemas or ontologies automatically. In this paper, we exploit six
general-purpose knowledge graphs as sources of background knowledge for the
matching task. The background sources are evaluated by applying three different
exploitation strategies. We find that explicit strategies still outperform
latent ones and that the choice of the strategy has a greater impact on the
final alignment than the actual background dataset on which the strategy is
applied. While we could not identify a universally superior resource, BabelNet
achieved consistently good results. Our best matcher configuration with
BabelNet performs very competitively when compared to other matching systems
even though no dataset-specific optimizations were made.



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