通过基于案例和概念的检索改进搜救规划和资源分配

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Information Systems Pub Date : 2024-06-01 DOI:10.1007/s10844-024-00861-0
Wajeeha Nasar, Ricardo da Silva Torres, Odd Erik Gundersen, Anniken Susanne Thoresen Karlsen
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引用次数: 0

摘要

由于气候变化的影响日益加剧,灾害发生的频率和严重程度都在增加,因此,有效和高效的搜救行动比以往任何时候都更加重要。认识到专家个人知识和过往经验的价值,我们在本文中介绍了如何将过往知识和专家经验与当前搜救实践有效结合,以改进救援规划和资源分配的调查结果。本文的一个特别重点是研究和论证将知识图谱和基于案例的推理作为搜救决策支持的一种可行方法的潜力。作为研究的一部分,我们利用挪威的搜救数据集以及基于案例和概念的相似性检索实施了一个示范系统。本文的主要贡献在于深入探讨了如何设计基于案例和概念的检索服务,以提高搜救计划的有效性。为了评估已排序案例的有效性,即它们如何与搜救专家的现有知识和见解保持一致,我们使用了精确度和召回率等评估指标。在评估过程中,我们发现救援行动类型等属性的精确度较高,而与涉及对象相关的精确度则相对较低。我们在评估过程中得出的主要结论是,基于知识的创建以及基于案例和概念的相似性检索服务可有助于优化搜救计划时间,并根据搜救事件描述分配适当的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Improving search and rescue planning and resource allocation through case-based and concept-based retrieval

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.

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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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