OPGEN: A System to Identify and Evaluate Potential Applications of Knowledge Based Systems

1992 ◽  
Author(s):  
Samir Rohatgi ◽  
James H. Oliver ◽  
Stuart S. Chen
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Knowledge Based Systems ◽  
The Core ◽  
Knowledge Based ◽  
Evaluation Functions ◽  
Potential Applications ◽  
Computer Based ◽  
Suitable System ◽  
Overall Effectiveness

Abstract This paper describes the development of OPGEN (Opportunity Generator), a computer based system to help identify areas where a knowledge based system (KBS) might be beneficial, and to evaluate whether a suitable system could be developed in that area. The core of the system is a knowledge base used to carry out the identification and evaluation functions. Ancillary functions serve to introduce and demonstrate KBS technology to enhance the overall effectiveness of the system. All aspects of the development, from knowledge acquisition through to testing are presented in this paper.

Rapid development of knowledge-based systems via integrated knowledge acquisition

2003 ◽  
Vol 17 (3) ◽  
pp. 221-234 ◽  
Author(s):  
HAO XING ◽  
SAMUEL H. HUANG ◽  
J. SHI
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Traditional Approach ◽  
Rapid Development ◽  
Real Life ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Linguistic Rules

This paper presents a novel approach, which is based on integrated (automatic/interactive) knowledge acquisition, to rapidly develop knowledge-based systems. Linguistic rules compatible with heuristic expert knowledge are used to construct the knowledge base. A fuzzy inference mechanism is used to query the knowledge base for problem solving. Compared with the traditional interview-based knowledge acquisition, our approach is more flexible and requires a shorter development cycle. The traditional approach requires several rounds of interviews (both structured and unstructured). However, our method involves an optional initial interview, followed by data collection, automatic rule generation, and an optional final interview/rule verification process. The effectiveness of our approach is demonstrated through a benchmark case study and a real-life manufacturing application.

Knowledge-Acquisition Tools for Medical Knowledge-Based Systems

1995 ◽  
Vol 34 (01/02) ◽  
pp. 25-39 ◽  
Author(s):  
G. Lanzola ◽  
S. Quaglini ◽  
M. Stefanelli
Keyword(s):  
Knowledge Acquisition ◽  
Medical Knowledge ◽  
Knowledge Based Systems ◽  
Medical Problems ◽  
Computational Framework ◽  
Domain Experts ◽  
Knowledge Based ◽  
Strategic Issue ◽  
Epistemological Model ◽  
Medical Task

Abstract:Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

Knowledge Extraction from Domain-Specific Documents

2017 ◽  
pp. 106-123
Author(s):  
Ram Kumar ◽  
Shailesh Jaloree ◽  
R. S. Thakur
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Domain Ontology ◽  
Knowledge Based Systems ◽  
Domain Specific ◽  
Knowledge Based ◽  
Representation Systems ◽  
Terminological Confusion ◽  
Knowledge Representation Systems

Knowledge-based systems have become widespread in modern years. Knowledge-base developers need to be able to share and reuse knowledge bases that they build. As a result, interoperability among different knowledge-representation systems is essential. Domain ontology seeks to reduce conceptual and terminological confusion among users who need to share various kind of information. This paper shows how these structures make it possible to bridge the gap between standard objects and Knowledge-based Systems.

Decision-Making Support Systems

2005 ◽  
pp. 759-765 ◽  
Author(s):  
Guisseppi Forgionne ◽  
Manuel Mora ◽  
Jatinder N.D. Gupta ◽  
Ovsei Gelman
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Support Systems ◽  
Decision Makers ◽  
Knowledge Based Systems ◽  
Executive Information Systems ◽  
Knowledge Based ◽  
Computer Based ◽  
Decision Making Support

Decision-making support systems (DMSS) are specialized computer-based information systems designed to support some, several or all phases of the decision-making process (Forgionne et al., 2000). They have the stand-alone or integrated capabilities of decision support systems (DSS), executive information systems (EIS) and expert systems/knowledge based systems (ES/KBS). Individual EIS, DSS, and ES/KBS, or pair-integrated combinations of these systems, have yielded substantial benefits for decision makers in real applications.

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