PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph
Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insigh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8142 |
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| author | Naeima Hamed Omer Rana Pablo Orozco-terWengel Benoît Goossens Charith Perera |
| author_facet | Naeima Hamed Omer Rana Pablo Orozco-terWengel Benoît Goossens Charith Perera |
| author_sort | Naeima Hamed |
| collection | DOAJ |
| description | Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood. Using elephant GPS data extracted from an ontology-based knowledge graph, PoachNet employs a sequential neural network to predict future movements, which are semantically modelled and incorporated into the graph. Semantic Web Rule Language (SWRL) is applied to infer poaching risk based on these geo-location predictions and poaching rule-based logic. By addressing spatiotemporal complexity and integrating predictions into an actionable semantic rule, PoachNet advances the field, with its geo-location prediction model outperforming state-of-the-art approaches. |
| format | Article |
| id | doaj-art-7c992eed15ae4c8dbe067ae864de1abd |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-7c992eed15ae4c8dbe067ae864de1abd2024-12-27T14:53:07ZengMDPI AGSensors1424-82202024-12-012424814210.3390/s24248142PoachNet: Predicting Poaching Using an Ontology-Based Knowledge GraphNaeima Hamed0Omer Rana1Pablo Orozco-terWengel2Benoît Goossens3Charith Perera4School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UKSchool of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UKSchool of Biosciences, Cardiff University, Cardiff CF10 3AX, UKSchool of Biosciences, Cardiff University, Cardiff CF10 3AX, UKSchool of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UKPoaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood. Using elephant GPS data extracted from an ontology-based knowledge graph, PoachNet employs a sequential neural network to predict future movements, which are semantically modelled and incorporated into the graph. Semantic Web Rule Language (SWRL) is applied to infer poaching risk based on these geo-location predictions and poaching rule-based logic. By addressing spatiotemporal complexity and integrating predictions into an actionable semantic rule, PoachNet advances the field, with its geo-location prediction model outperforming state-of-the-art approaches.https://www.mdpi.com/1424-8220/24/24/8142wildlifepoachingknowledge graphdeep learningpredictive analytics |
| spellingShingle | Naeima Hamed Omer Rana Pablo Orozco-terWengel Benoît Goossens Charith Perera PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph Sensors wildlife poaching knowledge graph deep learning predictive analytics |
| title | PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph |
| title_full | PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph |
| title_fullStr | PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph |
| title_full_unstemmed | PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph |
| title_short | PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph |
| title_sort | poachnet predicting poaching using an ontology based knowledge graph |
| topic | wildlife poaching knowledge graph deep learning predictive analytics |
| url | https://www.mdpi.com/1424-8220/24/24/8142 |
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