An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks
Location-Based Social Networks (LBSNs) contain rich information that can be used to identify and annotate points of interest (POIs). Discovering these POIs and annotating them with this information is not only helpful for understanding the social behavior of users, but it also provides benefits for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/8/11/487 |
| _version_ | 1811200030497832960 |
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| author | Zhiqiang Zou Xu He A-Xing Zhu |
| author_facet | Zhiqiang Zou Xu He A-Xing Zhu |
| author_sort | Zhiqiang Zou |
| collection | DOAJ |
| description | Location-Based Social Networks (LBSNs) contain rich information that can be used to identify and annotate points of interest (POIs). Discovering these POIs and annotating them with this information is not only helpful for understanding the social behavior of users, but it also provides benefits for location recommendations. However, current methods still have some limitations, such as a long annotating time and a low annotating accuracy. In this study, we develop a hybrid method to annotate POIs with meaningful information from LBSNs. The method integrates three patterns: temporal, spatial, and text patterns. Firstly, we present an approach for preprocessing data based on temporal patterns. Secondly, we describe a way to discover POIs through spatial patterns. Thirdly, we build a keyword dictionary for discovering the categories of POIs to be annotated via mining the text patterns. Finally, we integrate these three patterns to label each POI. Taking New York and London as the target areas, we accomplish automatic POI annotation by using Precision, Recall, and F-values to evaluate the effectiveness. The results show that our F-value is 78%, which is superior to that of the baseline method (Falcone’s method) at 73% and this suggests that our method is effective in extracting POIs and assigning them categories. |
| first_indexed | 2024-04-12T01:56:59Z |
| format | Article |
| id | doaj.art-544072e7f23a4d3199e89de7fe953bca |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-04-12T01:56:59Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-544072e7f23a4d3199e89de7fe953bca2022-12-22T03:52:46ZengMDPI AGISPRS International Journal of Geo-Information2220-99642019-10-0181148710.3390/ijgi8110487ijgi8110487An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social NetworksZhiqiang Zou0Xu He1A-Xing Zhu2College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaJiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, School of Geography, Nanjing Normal University, Nanjing 210023, ChinaLocation-Based Social Networks (LBSNs) contain rich information that can be used to identify and annotate points of interest (POIs). Discovering these POIs and annotating them with this information is not only helpful for understanding the social behavior of users, but it also provides benefits for location recommendations. However, current methods still have some limitations, such as a long annotating time and a low annotating accuracy. In this study, we develop a hybrid method to annotate POIs with meaningful information from LBSNs. The method integrates three patterns: temporal, spatial, and text patterns. Firstly, we present an approach for preprocessing data based on temporal patterns. Secondly, we describe a way to discover POIs through spatial patterns. Thirdly, we build a keyword dictionary for discovering the categories of POIs to be annotated via mining the text patterns. Finally, we integrate these three patterns to label each POI. Taking New York and London as the target areas, we accomplish automatic POI annotation by using Precision, Recall, and F-values to evaluate the effectiveness. The results show that our F-value is 78%, which is superior to that of the baseline method (Falcone’s method) at 73% and this suggests that our method is effective in extracting POIs and assigning them categories.https://www.mdpi.com/2220-9964/8/11/487location-based social networksdata miningpoints of interestflickrpoints of interest annotation |
| spellingShingle | Zhiqiang Zou Xu He A-Xing Zhu An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks ISPRS International Journal of Geo-Information location-based social networks data mining points of interest flickr points of interest annotation |
| title | An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks |
| title_full | An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks |
| title_fullStr | An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks |
| title_full_unstemmed | An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks |
| title_short | An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks |
| title_sort | automatic annotation method for discovering semantic information of geographical locations from location based social networks |
| topic | location-based social networks data mining points of interest flickr points of interest annotation |
| url | https://www.mdpi.com/2220-9964/8/11/487 |
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