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This article describes the system development processes for an electronic case library, built to facilitate and support the suicide prevention service provided by a social service organization in Hong Kong. It is a knowledge management system in which both ‘rule-based reasoning’ (RBR) and ‘case-based reasoning’ (CBR) are adopted. Initial test results, potentials and limitations of this knowledge management approach are discussed. [ABSTRACT FROM PUBLISHER]

Abstract: 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. [ABSTRACT FROM AUTHOR]