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Liuchang Xu, Ruichen Mao, Chengkun Zhang, Yuanyuan Wang, Xinyu Zheng, Xingyu Xue and Fang Xia
Address matching, which aims to match an input descriptive address with a standard address in an address database, is a key technology for achieving data spatialization. The construction of today?s smart cities depends heavily on the precise matching of ...
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Guangming Ling, Xiaofeng Mu, Chao Wang and Aiping Xu
Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenar...
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Rongke Wei, Haodong Pei, Dongjie Wu, Changwen Zeng, Xin Ai and Huixian Duan
The task of 3D reconstruction of urban targets holds pivotal importance for various applications, including autonomous driving, digital twin technology, and urban planning and development. The intricate nature of urban landscapes presents substantial cha...
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Mohammed Suleiman Mohammed Rudwan and Jean Vincent Fonou-Dombeu
Ontology alignment has become an important process for identifying similarities and differences between ontologies, to facilitate their integration and reuse. To this end, fuzzy string-matching algorithms have been developed for strings similarity detect...
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Fengying Jin, Rui Li, Jianyuan Liang, Xianyuan Zhang, Huaqiao Xing, Zhipeng Gui and Huayi Wu
The service network is capable of addressing large-scale service composition. However, existing service network works still have several limitations. Prior knowledge, such as expert-defined service chains, is not incorporated into the service network. Qo...
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Rodrigo Smarzaro, Clodoveu A. Davis, Jr. and José Alberto Quintanilha
One of the most significant challenges in cities concerns urban mobility. Urban mobility involves the use of different modes of transport, which can be individual or collective, and different organizations can produce their respective datasets that, usua...
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Jifang Wu, Jianghua Lv, Haoming Guo and Shilong Ma
Ontology Matching (OM) is performed to find semantic correspondences between the entity elements of different ontologies to enable semantic integration, reuse, and interoperability. Representation learning techniques have been introduced to the field of ...
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