scholarly journals A Data-Driven Knowledge Acquisition System: An End-to-End Knowledge Engineering Process for Generating Production Rules

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 15587-15607 ◽  
Author(s):  
Maqbool Ali ◽  
Rahman Ali ◽  
Wajahat Ali Khan ◽  
Soyeon Caren Han ◽  
Jaehun Bang ◽  
...  
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Engineering ◽  
Data Driven ◽  
Acquisition System ◽  
Engineering Process ◽  
Production Rules ◽  
End To End

Graphical Access to Medical Expert Systems: III. Design of a Knowledge Acquisition Environment*)

1987 ◽  
Vol 26 (03) ◽  
pp. 78-88 ◽  
Author(s):  
Joan Walton ◽  
M. A. Musen ◽  
D. M. Combs ◽  
C. D. Lane ◽  
E. H. Shortliffe ◽  
...  
Keyword(s):  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Engineering ◽  
Interactive Graphics ◽  
Engineering Process ◽  
Medical Expert ◽  
Domain Experts ◽  
Computer Scientists ◽  
Chemotherapy Protocol ◽  
Medical Expert Systems

SummaryKnowledge acquisition for expert systems typically is a tedious, iterative process involving long hours of consultation between the domain experts and the computer scientists who serve as knowledge engineers. For well-understood domains, however, it may be possible to facilitate the knowledge acquisition process by allowing domain experts to develop and edit a knowledge base directly. Administration of protocol-directed cancer chemotherapy is such a well-understood application area, and a knowledge acquisition system, called OPAL, has been developed for eliciting chemotherapy-protocol knowledge directly from expert oncologists. OPAL’s knowledge acquisition approach is based on the interactive graphics environment available on current generation workstations. The use of graphics improves the interface by reducing typing, avoiding natural language interpretations, and allowing flexibility in entry sequence. The knowledge in OPAL is displayed using an arrangement of hierarchically related, graphical forms. The position of a particular form in the hierarchy defines the context of the knowledge contained in the form. Intelligent editing programs such as OPAL can streamline the knowledge engineering process for highly structured domains requiring repetitive knowledge entry.

scholarly journals A META-TOOL TO SUPPORT THE DEVELOPMENT OF KNOWLEDGE ENGINEERING METHODOLOGIES AND PROJECTS

2012 ◽  
Vol 22 (08) ◽  
pp. 1055-1083
Author(s):  
JOSÉ ELOY FLÓREZ ◽  
JAVIER CARBÓ ◽  
FERNANDO FERNÁNDEZ
Keyword(s):  
Knowledge Acquisition ◽  
Ad Hoc ◽  
Knowledge Engineering ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Combined Application ◽  
Processing Information ◽  
Knowledge Acquisition Methods ◽  
Applied Knowledge

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

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