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

Author

Listed:
  • 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
    Fraunhofer Institute for Applied Information Technology FIT, 53757 Sankt Augustin, Germany)

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.

Suggested Citation

  • Zhiyu Pan & Guanchen Pan & Antonello Monti, 2022. "Semantic-Similarity-Based Schema Matching for Management of Building Energy Data," Energies, MDPI, vol. 15(23), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8894-:d:983389
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    References listed on IDEAS

    as
    1. Marco Pau & Panagiotis Kapsalis & Zhiyu Pan & George Korbakis & Dario Pellegrino & Antonello Monti, 2022. "MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain," Energies, MDPI, vol. 15(7), pages 1-22, April.
    2. Balaji, Bharathan & Bhattacharya, Arka & Fierro, Gabriel & Gao, Jingkun & Gluck, Joshua & Hong, Dezhi & Johansen, Aslak & Koh, Jason & Ploennigs, Joern & Agarwal, Yuvraj & Bergés, Mario & Culler, Davi, 2018. "Brick : Metadata schema for portable smart building applications," Applied Energy, Elsevier, vol. 226(C), pages 1273-1292.
    3. Marco Pritoni & Drew Paine & Gabriel Fierro & Cory Mosiman & Michael Poplawski & Avijit Saha & Joel Bender & Jessica Granderson, 2021. "Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis," Energies, MDPI, vol. 14(7), pages 1-37, April.
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