Abstract
Schema matching aims to establish the correspondence between the attributes of database schemas. It has been regarded as the most difficult and crucial stage in the development of many contemporary database and web semantic systems. Manual mapping is a lengthy and laborious process, yet a low-quality algorithmic matcher may cause more trouble. Moreover, the issue of data privacy in certain domains, such as healthcare, poses further challenges, as the use of instance-level data should be avoided to prevent the leakage of sensitive information. To address this issue, we propose CONSchema, a model that combines both the textual attribute description and constraints of the schemas to learn a better matcher. We also propose a new experimental setting to assess the practical performance of schema matching models. Our results on 6 benchmark datasets across various domains including healthcare and movies demonstrate the robustness of CONSchema.
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Notes
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We explored other models such as random forest and logistic regression and the results follow similar trends with MLP providing the largest performance boost.
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This work was supported by the National Science Foundation award IIS-2145411.
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Wu, K., Zhang, J., Ho, J.C. (2023). CONSchema: Schema Matching with Semantics and Constraints. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_21
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