Schema matching based on energy domain pre-trained language model

Energy Informatics

Research

Zhiyu Pan¹  Muchen Yang¹  Antonello Monti² 

[1] Institute for Automation of Complex Power Systems, RWTH Aachen University, Mathieustraße 10, 52074, Aachen, Germany;Institute for Automation of Complex Power Systems, RWTH Aachen University, Mathieustraße 10, 52074, Aachen, Germany;Fraunhofer FIT, Schloss Birlinghoven, 53757, Sankt Augustin, Germany;

关键词: Pre-trained language model;    Schema matching;    Energy domain;   

DOI  :  10.1186/s42162-023-00277-0

来源: Springer

【 摘 要 】

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 developed two energy domain language models, Energy BERT and Energy Sentence Bert, and trained them using an open-source scientific corpus. The comparison of the developed models with the baseline model using real-life energy domain data shows that Energy BERT and Energy Sentence Bert models significantly improve the accuracy of schema matching.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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