Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts
The ubiquitous spatiotemporal information extracted from Internet texts limits its application in spatiotemporal association and analysis due to its unstructured nature and uncertainty. This study uses ST-Voxel modeling to solve the problem of structured modeling and the association of ubiquitous sp...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2023.2185692 |
| _version_ | 1797678463435407360 |
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| author | Dali Wang Xiaochong Tong Chenguang Dai Congzhou Guo Yi Lei Chunping Qiu He Li Yuekun Sun |
| author_facet | Dali Wang Xiaochong Tong Chenguang Dai Congzhou Guo Yi Lei Chunping Qiu He Li Yuekun Sun |
| author_sort | Dali Wang |
| collection | DOAJ |
| description | The ubiquitous spatiotemporal information extracted from Internet texts limits its application in spatiotemporal association and analysis due to its unstructured nature and uncertainty. This study uses ST-Voxel modeling to solve the problem of structured modeling and the association of ubiquitous spatiotemporal information in natural language texts. It provides a new solution for associating ubiquitous spatiotemporal information on the Internet and discovering public opinion. The main contributions of this paper include: (1) It proposes a convolved method for ST-Voxel, which solves the voxel modeling problem of unstructured and uncertain spatiotemporal objects and spatiotemporal relation in natural language texts. Experiments show that this method can effectively model 5 types of spatiotemporal objects and 16 types of uncertain spatiotemporal relation founded in texts; (2) It realizes the unknown event discovery based on voxelized spatiotemporal information association. Experiments show that this method can effectively solve the aggregation of ubiquitous spatiotemporal information in multi-natural language texts, which is conducive to discovering spatiotemporal events. The selection of convolution parameters in voxel modeling is also discussed. A parameter selection method for balancing the discovery capability and discovery accuracy of spatiotemporal events is given. |
| first_indexed | 2024-03-11T23:00:08Z |
| format | Article |
| id | doaj.art-01662b30197a47f2b0b955e38724e537 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:08Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-01662b30197a47f2b0b955e38724e5372023-09-21T14:57:12ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552023-12-0116186889010.1080/17538947.2023.21856922185692Voxel modeling and association of ubiquitous spatiotemporal information in natural language textsDali Wang0Xiaochong Tong1Chenguang Dai2Congzhou Guo3Yi Lei4Chunping Qiu5He Li6Yuekun Sun7Information Engineering UniversityInformation Engineering UniversityInformation Engineering UniversityInformation Engineering UniversityInformation Engineering UniversityInformation Engineering UniversityInformation Engineering UniversityInformation Engineering UniversityThe ubiquitous spatiotemporal information extracted from Internet texts limits its application in spatiotemporal association and analysis due to its unstructured nature and uncertainty. This study uses ST-Voxel modeling to solve the problem of structured modeling and the association of ubiquitous spatiotemporal information in natural language texts. It provides a new solution for associating ubiquitous spatiotemporal information on the Internet and discovering public opinion. The main contributions of this paper include: (1) It proposes a convolved method for ST-Voxel, which solves the voxel modeling problem of unstructured and uncertain spatiotemporal objects and spatiotemporal relation in natural language texts. Experiments show that this method can effectively model 5 types of spatiotemporal objects and 16 types of uncertain spatiotemporal relation founded in texts; (2) It realizes the unknown event discovery based on voxelized spatiotemporal information association. Experiments show that this method can effectively solve the aggregation of ubiquitous spatiotemporal information in multi-natural language texts, which is conducive to discovering spatiotemporal events. The selection of convolution parameters in voxel modeling is also discussed. A parameter selection method for balancing the discovery capability and discovery accuracy of spatiotemporal events is given.http://dx.doi.org/10.1080/17538947.2023.2185692spatiotemporal voxelsubiquitous spatiotemporal modelingspatiotemporal associationdiscrete grid |
| spellingShingle | Dali Wang Xiaochong Tong Chenguang Dai Congzhou Guo Yi Lei Chunping Qiu He Li Yuekun Sun Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts International Journal of Digital Earth spatiotemporal voxels ubiquitous spatiotemporal modeling spatiotemporal association discrete grid |
| title | Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts |
| title_full | Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts |
| title_fullStr | Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts |
| title_full_unstemmed | Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts |
| title_short | Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts |
| title_sort | voxel modeling and association of ubiquitous spatiotemporal information in natural language texts |
| topic | spatiotemporal voxels ubiquitous spatiotemporal modeling spatiotemporal association discrete grid |
| url | http://dx.doi.org/10.1080/17538947.2023.2185692 |
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