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| DOI | 10.1007/s10844-024-00861-0 |
| Improving search and rescue planning and resource allocation through case-based and concept-based retrieval | |
| 发表日期 | 2024 |
| ISSN | 0925-9902 |
| EISSN | 1573-7675 |
| 英文摘要 | The need for effective and efficient search and rescue operations is more important than ever as the frequency and severity of disasters increase due to the escalating effects of climate change. Recognizing the value of personal knowledge and past experiences of experts, in this paper, we present findings of an investigation of how past knowledge and experts' experiences can be effectively integrated with current search and rescue practices to improve rescue planning and resource allocation. A special focus is on investigating and demonstrating the potential associated with integrating knowledge graphs and case-based reasoning as a viable approach for search and rescue decision support. As part of our investigation, we have implemented a demonstrator system using a Norwegian search and rescue dataset and case-based and concept-based similarity retrieval. The main contribution of the paper is insight into how case-based and concept-based retrieval services can be designed to improve the effectiveness of search and rescue planning. To evaluate the validity of ranked cases in terms of how they align with the existing knowledge and insights of search and rescue experts, we use evaluation measures such as precision and recall. In our evaluation, we observed that attributes, such as the rescue operation type, have high precision, while the precision associated with the objects involved is relatively low. Central findings from our evaluation process are that knowledge-based creation, as well as case- and concept-based similarity retrieval services, can be beneficial in optimizing search and rescue planning time and allocating appropriate resources according to search and rescue incident descriptions. |
| 英文关键词 | Case-based; Cased-based reasoning (CBR); Concept-based; Knowledge graphs (KG); Rescue planning, Resource allocation; Similarity Retrieval |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems |
| WOS记录号 | WOS:001236511500001 |
| 来源期刊 | JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/301221 |
| 作者单位 | Norwegian University of Science & Technology (NTNU); Norwegian University of Science & Technology (NTNU); Wageningen University & Research |
| 推荐引用方式 GB/T 7714 | . Improving search and rescue planning and resource allocation through case-based and concept-based retrieval[J],2024. |
| APA | (2024).Improving search and rescue planning and resource allocation through case-based and concept-based retrieval.JOURNAL OF INTELLIGENT INFORMATION SYSTEMS. |
| MLA | "Improving search and rescue planning and resource allocation through case-based and concept-based retrieval".JOURNAL OF INTELLIGENT INFORMATION SYSTEMS (2024). |
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