![]() |
|||
![]()
|
![]() |
![]() |
![]() |
7.6. EXPERTISE APPLICATIONS OF EXPERT SYSTEMSKnowledge repository: A knowledge repository (Beckman, 1996) is an online, computer-based storehouse of expertise, knowledge, experience, and documentation about a particular domain of expertise. In creating a knowledge repository, knowledge is collected, summarized, and integrated across information sources. Knowledge acquisition, a discipline related to expert systems, consists of a collection of techniques that are used to elicit implicit knowledge from a domain expert. In conjunction with knowledge representation, these concepts can be used to collect, organize, integrate, and summarize knowledge about a particular speciality/domain. A knowledge repository consists of many different kinds of knowledge and information:
Integrated performance support systems: We have been discussing the application of ES at the microlevel in an organization. It is also possible to consider the totality of workforce needs by designing Integrated Performance Support Systems (IPSS) that make use of various AI disciplines (Winslow and Bramer, 1994). IPSS can be categorized into the following services (after Winslow and Bramer):
In addition to ES, other AI disciplines involved in providing IPSS include intelligent tutoring, knowledge discovery and learning, intelligent information retrieval, smart form templates, uncertainty, as well as speech recognition, intelligent OCR, natural language understanding through conceptual dependencies, and text generation. 8. THE FUTURE OF EXPERT SYSTEMS IN BUSINESS -- ACHIEVING THE IDEAL ENTERPRISEThe future of ES in business reengineering is bright. The convergence of ES and reengineering in the redesign of an enterprise provide powerful sustainable competitive advantages. By applying the ideas and methods in this chapter, leading-edge organizations can achieve market leadership, customer intimacy, superior operational performance, as well as higher profit margins. The transition to an ideal future capability can evolve through four sequential, but overlapping stages:
8.1. STAGE 1: IS AND IT INFRASTRUCTUREIt is not enough to identify and develop isolated ES applications for reengineering. The prerequisite is that there must be a networked IT platform installed across the organization to support the ES applications. Every employee is equipped with a workstation that supports complex computational, informational, and communication needs. Every employee can communicate electronically with all other employees, both as individuals and collaboratively in groups. Powerful system navigation and information exploration tools are available that use flexible keyword search, hypermedia, dynamic visual querying, and tree maps (Shneiderman, 1992). Every employee is provided with a suite of standard office automation software, including text processing, presentation graphics, spreadsheets, relational DBMS, calendaring, meeting schedulers, Web browsers, E-mail, voice-mail, and fax-mail. This standard software should be integrated with the custom IS. 8.2. STAGE 2: KNOWLEDGE REPOSITORIESIn stage 2, enterprise-wide relational and object models are created, populated, and regulated. Consistent corporate-wide data or object dictionaries are created. Existing online data are transformed and reformatted onto relational databases, or wrappered for object models. Because most data were previously kept on paper, a massive data entry effort may be required to populate the databases. Smart data-entry templates are available to ensure quality by checking for entity, validity, consistency, and reality. Later, multimedia object repositories that hold data, text, graphics, images, full-motion video, audio, objects, cases, and rules have replaced relational data models. Knowledge repositories are built for special-purpose, functional, corporate, and external uses. ES and other AI software exist to translate from most media into text and to understand what the text means using intelligent OCR for document images, voice recognition for audio, vision understanding into textual features, and from text into meaning using natural language understanding.
|
![]() |
|
Use of this site is subject certain Terms & Conditions. Copyright (c) 1996-1999 EarthWeb, Inc.. All rights reserved. Reproduction in whole or in part in any form or medium without express written permission of EarthWeb is prohibited. Please read our privacy policy for details. |