Schema matching based on energy domain pre-trained language model
Zhiyu Pan,
Muchen Yang,
Antonello Monti
Abstract:Data integration in the energy sector, which refers to the process of combining and harmonizing data from multiple heterogeneous sources, is becoming increasingly difficult due to the growing volume of heterogeneous data. Schema matching plays a crucial role in this process by giving each representation a unique identity by matching raw energy data to a generic data model. This study uses an energy domain language model to automate schema matching, reducing manual effort in integrating heterogeneous data. We d… Show more
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