A Hierarchical Spatiotemporal Data Model Based on Knowledge Graphs for Representation and Modeling of Dynamic Landslide Scenes Article Swipe
Juan Li
,
Jin Zhang
,
Li Wang
,
Aobo Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/su162310271
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/su162310271
Represention and modeling the dynamic landslide scenes is essential for gaining a comprehensive understanding and managing them effectively. Existing models, which focus on a single scale make it difficult to fully express the complex, multi-scale spatiotemporal process within landslide scenes. To address these issues, we proposed a hierarchical spatiotemporal data model, named as HSDM, to enhance the representation for geographic scenes. Specifically, we introduced a spatiotemporal object model that integrates both their structural and process information of objects. Furthermore, we extended the process definition to capture complex spatiotemporal processes. We sorted out the relationships used in HSDM and defined four types of spatiotemporal correlation relations to represent the connections between spatiotemporal objects. Meanwhile, we constructed a three-level graph model of geographic scenes based on these concepts and relationships. Finally, we achieved representation and modeling of a dynamic landslide scene in Heifangtai using HSDM and implemented complex querying and reasoning with Neo4j’s Cypher language. The experimental results demonstrate our model’s capabilities in modeling and reasoning about complex multi-scale information and spatio-temporal processes with landslide scenes. Our work contributes to landslide knowledge representation, inventory and dynamic simulation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su162310271
- OA Status
- gold
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404702413
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404702413Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su162310271Digital Object Identifier
- Title
-
A Hierarchical Spatiotemporal Data Model Based on Knowledge Graphs for Representation and Modeling of Dynamic Landslide ScenesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-11-23Full publication date if available
- Authors
-
Juan Li, Jin Zhang, Li Wang, Aobo ZhaoList of authors in order
- Landing page
-
https://doi.org/10.3390/su162310271Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/su162310271Direct OA link when available
- Concepts
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Representation (politics), Landslide, Computer science, Artificial intelligence, Hierarchical database model, Data mining, Theoretical computer science, Geology, Geotechnical engineering, Law, Political science, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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36Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scale | 25 |
| abstract_inverted_index.scene | 138 |
| abstract_inverted_index.their | 71 |
| abstract_inverted_index.these | 42, 124 |
| abstract_inverted_index.types | 100 |
| abstract_inverted_index.using | 141 |
| abstract_inverted_index.which | 20 |
| abstract_inverted_index.Cypher | 151 |
| abstract_inverted_index.model, | 50 |
| abstract_inverted_index.object | 66 |
| abstract_inverted_index.scenes | 6, 121 |
| abstract_inverted_index.single | 24 |
| abstract_inverted_index.sorted | 90 |
| abstract_inverted_index.within | 37 |
| abstract_inverted_index.address | 41 |
| abstract_inverted_index.between | 109 |
| abstract_inverted_index.capture | 85 |
| abstract_inverted_index.complex | 86, 145, 165 |
| abstract_inverted_index.defined | 98 |
| abstract_inverted_index.dynamic | 4, 136, 183 |
| abstract_inverted_index.enhance | 55 |
| abstract_inverted_index.express | 31 |
| abstract_inverted_index.gaining | 10 |
| abstract_inverted_index.issues, | 43 |
| abstract_inverted_index.models, | 19 |
| abstract_inverted_index.process | 36, 74, 82 |
| abstract_inverted_index.results | 155 |
| abstract_inverted_index.scenes. | 39, 60, 173 |
| abstract_inverted_index.Existing | 18 |
| abstract_inverted_index.Finally, | 128 |
| abstract_inverted_index.achieved | 130 |
| abstract_inverted_index.complex, | 33 |
| abstract_inverted_index.concepts | 125 |
| abstract_inverted_index.extended | 80 |
| abstract_inverted_index.managing | 15 |
| abstract_inverted_index.modeling | 2, 133, 161 |
| abstract_inverted_index.objects. | 77, 111 |
| abstract_inverted_index.proposed | 45 |
| abstract_inverted_index.querying | 146 |
| abstract_inverted_index.Neo4j’s | 150 |
| abstract_inverted_index.difficult | 28 |
| abstract_inverted_index.essential | 8 |
| abstract_inverted_index.inventory | 181 |
| abstract_inverted_index.knowledge | 179 |
| abstract_inverted_index.landslide | 5, 38, 137, 172, 178 |
| abstract_inverted_index.language. | 152 |
| abstract_inverted_index.model’s | 158 |
| abstract_inverted_index.processes | 170 |
| abstract_inverted_index.reasoning | 148, 163 |
| abstract_inverted_index.relations | 104 |
| abstract_inverted_index.represent | 106 |
| abstract_inverted_index.Heifangtai | 140 |
| abstract_inverted_index.Meanwhile, | 112 |
| abstract_inverted_index.definition | 83 |
| abstract_inverted_index.geographic | 59, 120 |
| abstract_inverted_index.integrates | 69 |
| abstract_inverted_index.introduced | 63 |
| abstract_inverted_index.processes. | 88 |
| abstract_inverted_index.structural | 72 |
| abstract_inverted_index.connections | 108 |
| abstract_inverted_index.constructed | 114 |
| abstract_inverted_index.contributes | 176 |
| abstract_inverted_index.correlation | 103 |
| abstract_inverted_index.demonstrate | 156 |
| abstract_inverted_index.implemented | 144 |
| abstract_inverted_index.information | 75, 167 |
| abstract_inverted_index.multi-scale | 34, 166 |
| abstract_inverted_index.simulation. | 184 |
| abstract_inverted_index.three-level | 116 |
| abstract_inverted_index.Furthermore, | 78 |
| abstract_inverted_index.Represention | 0 |
| abstract_inverted_index.capabilities | 159 |
| abstract_inverted_index.effectively. | 17 |
| abstract_inverted_index.experimental | 154 |
| abstract_inverted_index.hierarchical | 47 |
| abstract_inverted_index.Specifically, | 61 |
| abstract_inverted_index.comprehensive | 12 |
| abstract_inverted_index.relationships | 93 |
| abstract_inverted_index.understanding | 13 |
| abstract_inverted_index.relationships. | 127 |
| abstract_inverted_index.representation | 57, 131 |
| abstract_inverted_index.spatiotemporal | 35, 48, 65, 87, 102, 110 |
| abstract_inverted_index.representation, | 180 |
| abstract_inverted_index.spatio-temporal | 169 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5101620335 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I9086337 |
| citation_normalized_percentile.value | 0.25153573 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |