Abstract
Multisource point of interest (POI) attribute alignment is the consistent processing of heterogeneous attribute values from different data sources pointing to the same POI data, which is one of the key technologies to achieve geospatial data fusion. However, semantic heterogeneity problems of synonyms and homographs among different POI data sources are encountered, which makes multisource POI data fusion challenging. This paper proposes a multisource POI attribute alignment method based on the Enhanced Semantic Representation Model (ESRM). First, the unlabeled corpus is preprocessed by Chinese word segmentation and attribute expression sequence construction. Then, the ESRM is pre-trained using the relational consistency prediction and replacement language model tasks. Finally, the model is fine-tuned through supervised learning to perform the attribute alignment task for multisource POI data, as per the specific downstream tasks. We used the POI attributes of Baidu Map, Tencent Map, and Gaode Map in Chengdu, China as the experimental data. The findings demonstrate that the proposed model outperforms existing methods for attribute alignment. Specifically, the category attribute consistency achieves a Macro-F1 value of over 90%, and the address attribute standardization achieves a BLEU-4 score of over 95%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.