A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses
Accurate address matching is crucial for the analysis, integration, and intelligent management of urban geospatial data and is also a key step in achieving geocoding. However, due to the complexity, diversity, and irregularity of address expression, address matching becomes a challenging task. This...
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MDPI AG
2025-06-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/6/227 |
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| author | Pengpeng Li Qing Zhu Jiping Liu Tao Liu Ping Du Shuangtong Liu Yuting Zhang |
| author_facet | Pengpeng Li Qing Zhu Jiping Liu Tao Liu Ping Du Shuangtong Liu Yuting Zhang |
| author_sort | Pengpeng Li |
| collection | DOAJ |
| description | Accurate address matching is crucial for the analysis, integration, and intelligent management of urban geospatial data and is also a key step in achieving geocoding. However, due to the complexity, diversity, and irregularity of address expression, address matching becomes a challenging task. This paper proposes a multi-semantic feature fusion method for complex address matching of Chinese addresses that formulates address matching as a classification task that directly predicts whether two addresses refer to the same location, without relying on predefined similarity thresholds. First, the address is resolved into address elements, and the Word2vec model is trained to generate word vector representations using these address elements. Then, multi-semantic features of the addresses are extracted using a Text Recurrent Convolutional Neural Network (Text-RCNN) and a Graph Attention Network (GAT). Finally, the Enhanced Sequential Inference Model (ESIM) is used to perform both local inference and inference composition on the multi-semantic features of the addresses to achieve accurate matching of addresses. Experiments were conducted using Points of Interest (POI) address data from Baidu Maps, Tencent Maps, and Amap within the Chengdu area. The results demonstrate that the proposed method outperforms existing address matching methods, with precision, recall, and F1 values all exceeding 95%. In addition, transfer experiments using datasets from five other cities including Beijing, Shanghai, Xi’an, Guangzhou, and Wuhan show that the model maintains strong generalization ability, achieving F1 values above 84% in cities such as Xi’an and Wuhan. |
| format | Article |
| id | doaj-art-7642b9c2e0be467ca7174c6eac2e3b3e |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-7642b9c2e0be467ca7174c6eac2e3b3e2025-08-20T03:27:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-06-0114622710.3390/ijgi14060227A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese AddressesPengpeng Li0Qing Zhu1Jiping Liu2Tao Liu3Ping Du4Shuangtong Liu5Yuting Zhang6Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaResearch Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaAccurate address matching is crucial for the analysis, integration, and intelligent management of urban geospatial data and is also a key step in achieving geocoding. However, due to the complexity, diversity, and irregularity of address expression, address matching becomes a challenging task. This paper proposes a multi-semantic feature fusion method for complex address matching of Chinese addresses that formulates address matching as a classification task that directly predicts whether two addresses refer to the same location, without relying on predefined similarity thresholds. First, the address is resolved into address elements, and the Word2vec model is trained to generate word vector representations using these address elements. Then, multi-semantic features of the addresses are extracted using a Text Recurrent Convolutional Neural Network (Text-RCNN) and a Graph Attention Network (GAT). Finally, the Enhanced Sequential Inference Model (ESIM) is used to perform both local inference and inference composition on the multi-semantic features of the addresses to achieve accurate matching of addresses. Experiments were conducted using Points of Interest (POI) address data from Baidu Maps, Tencent Maps, and Amap within the Chengdu area. The results demonstrate that the proposed method outperforms existing address matching methods, with precision, recall, and F1 values all exceeding 95%. In addition, transfer experiments using datasets from five other cities including Beijing, Shanghai, Xi’an, Guangzhou, and Wuhan show that the model maintains strong generalization ability, achieving F1 values above 84% in cities such as Xi’an and Wuhan.https://www.mdpi.com/2220-9964/14/6/227address matchingmulti-semantic feature fusionText-RCNNGATESIM |
| spellingShingle | Pengpeng Li Qing Zhu Jiping Liu Tao Liu Ping Du Shuangtong Liu Yuting Zhang A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses ISPRS International Journal of Geo-Information address matching multi-semantic feature fusion Text-RCNN GAT ESIM |
| title | A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses |
| title_full | A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses |
| title_fullStr | A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses |
| title_full_unstemmed | A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses |
| title_short | A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses |
| title_sort | multi semantic feature fusion method for complex address matching of chinese addresses |
| topic | address matching multi-semantic feature fusion Text-RCNN GAT ESIM |
| url | https://www.mdpi.com/2220-9964/14/6/227 |
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