Semantic-Similarity-Based Schema Matching for Management of Building Energy Data

Zhiyu Pan (), Guanchen Pan and Antonello Monti
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Zhiyu Pan: Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany
Guanchen Pan: Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany
Antonello Monti: Institute for Automation of Complex Power Systems, RWTH Aachen University, 52074 Aachen, Germany

Energies, 2022, vol. 15, issue 23, 1-23

Abstract: The increase in heterogeneous data in the building energy domain creates a difficult challenge for data integration. Schema matching, which maps the raw data from the building energy domain to a generic data model, is the necessary step in data integration and provides a unique representation. Only a small amount of labeled data for schema matching exists and it is time-consuming and labor-intensive to manually label data. This paper applies semantic-similarity methods to the automatic schema-mapping process by combining knowledge from natural language processing, which reduces the manual effort in heterogeneous data integration. The active-learning method is applied to solve the lack-of-labeled-data problem in schema matching. The results of the schema matching with building-energy-domain data show the pre-trained language model provides a massive improvement in the accuracy of schema matching and the active-learning method greatly reduces the amount of labeled data required.

Keywords: semantic similarity; schema matching; active learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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