Plan-based information requirements: automated knowledge acquisition to support information management in an intelligent pilot-vehicle interface

2003 ◽  
Author(s):  
C.A. Miller ◽  
V. Shalin ◽  
N. Geddes ◽  
B. Hoshstrasser
Keyword(s):  
Knowledge Acquisition ◽  
Information Management ◽  
Information Requirements ◽  
Automated Knowledge ◽  
Support Information

Knowledge Geometry

2015 ◽  
Vol 14 (04) ◽  
pp. 1550028 ◽  
Author(s):  
Flávio Luis de Mello ◽  
Roberto Lins de Carvalho
Keyword(s):  
Knowledge Management ◽  
Information Processing ◽  
Knowledge Acquisition ◽  
Information Management ◽  
Spatial Representation ◽  
Geometric Approach ◽  
Alternative Theory ◽  
Acquisition Process ◽  
Evaluation Systems ◽  
Processing Information

This paper aims to present what we call knowledge geometry, an alternative theory for spatial representation of features related to information processing, information management, and knowledge management. It is a unique geometric approach for representing intuition, reification, interpretation, and deduction processes, as well as their relations. We employ the concept of cultural filter and use what we call real, conceptual, and symbolic planes in order to support transformations which occur along the perception of a phenomenon. After that, we discuss the use of evaluation systems to judge concepts and also the use of semantic systems as a communication language. Finally, a framework of the knowledge acquisition process in the field of the proposed theory is offered, proving the feasibility of its automation.

Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms

2005 ◽  
Vol 9 (6) ◽  
pp. 693-697 ◽  
Author(s):  
Nazar Elfadil ◽  
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Knowledge Acquisition ◽  
Training Data ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Data Set ◽  
Unsupervised Neural Network ◽  
Automated Knowledge ◽  
Self Organizing

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.

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