BERT-based User Attribute Mapping Framework in Heterogeneous Systems

Jahoon Koo, Yujun Kim, Young-Gab Kim

Abstract: The increasing application of information and communication technologies across various fields has led to the emergence of numerous heterogeneous systems, creating complex digital ecosystems where data integration and interoperability present significant challenges. This paper proposes a comprehensive framework for automated user attribute mapping that addresses these challenges, leveraging bidirectional encoder representations from transformers (BERT), a state-of-the-art natural language processing (NLP) model, to enable accurate mapping across systems with diverse data representations. The framework integrates a metadata registry (MDR) and blockchain to securely manage and share mapped information, ensuring consistent data integration across distributed environments. By overcoming the limitations of traditional rule-based methods, the proposed framework significantly improves system interoperability. The experimental results are provided to demonstrate the feasibility of the proposed mapping method, showcasing its practical applicability in real-world scenarios. These results confirm the framework's potential to enhance data integration and interoperability in complex, heterogeneous systems.

External IDs:doi:10.1145/3672608.3707834