Background Knowledge in Schema Matching: Strategy vs. Data
This work addresses schema matching for data integration, but it is incremental as it compares existing strategies and datasets without introducing new methods.
The paper tackled the problem of automatically matching schemas or ontologies by evaluating six general-purpose knowledge graphs and three exploitation strategies, finding that explicit strategies outperform latent ones and that strategy choice impacts alignment more than the dataset, with BabelNet achieving consistently good results and a competitive best configuration.
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.