An interactive address matching method based on a graph attention mechanism
Problem:: Modernizing and standardizing place names and addresses is a key challenge in the development of smart cities. Purpose:: This paper proposes a solution to address matching challenges, such as incomplete descriptions, reversed word order, and the diverse descriptions often found in Chinese...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-12-01
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| Series: | International Journal of Cognitive Computing in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266630742400055X |
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| Summary: | Problem:: Modernizing and standardizing place names and addresses is a key challenge in the development of smart cities. Purpose:: This paper proposes a solution to address matching challenges, such as incomplete descriptions, reversed word order, and the diverse descriptions often found in Chinese addresses. Method:: Leveraging the hierarchical structure of Chinese addresses, this study introduces the interactive address matching graph attention model (IAMGAM). In the IAMGAM, an attention-based feature interaction method (AFIM) is employed. To reflect the hierarchical nature of address elements, a directed graph is used to model the address data, and the model is trained and tested using a graph attention mechanism. Results:: Experiments demonstrate that the IAMGAM achieves an accuracy and F1-score of 99.61%. Compared with the existing address matching methods, the IAMGAM improves the accuracy by 0.66% to 2.57%, and the F1-score by 0.68% to 2.55%, outperforming baseline models. Additionally, ablation experiments confirm the effectiveness of each component within the model. Furthermore, when fine-tuned using ChatGLM2-6B, the results show that the IAMGAM still outperforms ChatGLM2-6B. Conclusion:: IAMGAM demonstrates excellent performance in Chinese address matching tasks, and the Large Language Model (LLM)-based methods, such as ChatGLM2-6B, show great potential for future development in this area. |
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| ISSN: | 2666-3074 |