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A narrative-based reasoning with applications in decision support for social service organizations

机译：基于叙事的推理及其在社会服务组织的决策支持中的应用

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摘要

Nowadays, there is an increasing demand for incorporating unstructured narratives in decision support for knowledge-intensive industries such as healthcare and social service organizations. However, most of the current research on decision support systems (DSS) mainly focused on dealing with structured data and are inadequate to dealing with unstructured narratives such as clients' records and stories. This paper presents a narrative-based reasoning (NBR) algorithm which incorporates the technologies of knowledge-based system (KBS), computational linguistics, and artificial intelligence (AI) for automatic processing unstructured narratives and inferring useful knowledge for decision support. A NBR enabled DSS has been built and was evaluated through a series of experiments conducted in early intervention of mental health of a social service company in Hong Kong. The performance of NBR was measured based on recall and precision and encouraging results were obtained. High recall and precision are achieved in the reasoning of unstructured data, and high recall is achieved for the association analysis. The results show that it is possible for inferring recommendations for problem solving from unstructured narratives automatically. Based on the approach, it helps to support knowledge workers with reliable suggestions on decision making so as to increase the quality of their solutions.
机译：如今，将非结构化叙事纳入对诸如医疗保健和社会服务组织等知识密集型行业的决策支持中的需求日益增长。但是，当前对决策支持系统（DSS）的大多数研究主要集中于处理结构化数据，并且不足以处理诸如客户记录和故事之类的非结构化叙述。本文提出了一种基于叙事的推理（NBR）算法，该算法结合了基于知识的系统（KBS），计算语言学和人工智能（AI）的技术，用于自动处理非结构化叙事并推断出用于决策支持的有用知识。已建立了启用NBR的DSS，并通过一系列对香港一家社会服务公司的心理健康进行早期干预的实验进行了评估。基于召回率和精度对NBR的性能进行了测量，并获得了令人鼓舞的结果。在非结构化数据的推理中实现了较高的查全率和准确性，而在关联分析中也实现了较高的查全率。结果表明，可以从非结构化叙述中自动推断出解决问题的建议。基于此方法，它有助于为知识工作者提供可靠的决策建议，从而提高他们的解决方案的质量。

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