Background Knowledge in Schema Matching: Strategy vs. Data

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