A frame knowledge system for managing financial decision knowledge

Weissor Shiue, Sheng-Tun Li, Kuan Ju Chen

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

Managing decision knowledge or expertise from domain experts is one of the most exciting challenges in today's knowledge management field. The nature of decision knowledge in determining a firm's financial health is context-dependent, intangible, and tacit in nature. Knowledge-based systems (KBS) have been recognized as a successful paradigm for managing financial decision knowledge attributed to possessing capabilities of reasoning and enhancing the consistency of decision-making. However, most present KBS adopt rules as the knowledge representation scheme, which cannot express the expert's knowledge construct systematically when dealing with more numerous and disordered knowledge connotations. In addition, the standalone nature of the systems hinders them from deploying onto heterogeneous platforms and cannot accommodate to the emerging Web-enabled environment. To reduce these flaws, this study proposes a frame knowledge system in which the structural and procedural decision knowledge is encapsulated so that unnecessary interference can be avoided. A protocol analysis, before encapsulation, is conducted to elicit the tacit and unstructured knowledge from a senior CPA we cooperated with. The deployment and Web enabling issue is tackled by using Jess and Java interoperable computing. With these combined, it is possible to prompt the understandability, accessibility, and reusability of KBS. The effectiveness of the proposed system is validated in supporting the expert's decision-making by conducting an empirical experimentation on 537 companies listed in the Taiwan Stock Exchange Corporation.

Original languageEnglish
Pages (from-to)1068-1079
Number of pages12
JournalExpert Systems With Applications
Volume35
Issue number3
DOIs
Publication statusPublished - 2008 Oct 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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