Introduction to Part II: Knowledge Acquisition and Knowledge Representation

2003 ◽  
pp. 67-69
Author(s):  
Paulo Quaresma ◽  
Akira Ishikawa ◽  
Rolf Schwitter
Keyword(s):  
Knowledge Representation ◽  
Knowledge Acquisition

A Computational Simulation of the Cognitive Process of Children Knowledge Acquisition and Memory Development

2012 ◽  
pp. 111-127
Author(s):  
Jeff Bancroft ◽  
Yingxu Wang
Keyword(s):  
Knowledge Representation ◽  
Knowledge Acquisition ◽  
Computational Simulation ◽  
Simulation Tool ◽  
Memory Development ◽  
Cognitive Mechanisms ◽  
Children's Memory ◽  
Mechanisms Of Memory ◽  
The Mathematical Model ◽  
The Brain

The cognitive mechanisms of knowledge representation, memory establishment, and learning are fundamental issues in understanding the brain. A basic approach to studying these mental processes is to observe and simulate how knowledge is memorized by little children. This paper presents a simulation tool for knowledge acquisition and memory development for young children of two to five years old. The cognitive mechanisms of memory, the mathematical model of concepts and knowledge, and the fundamental elements of internal knowledge representation are explored. The cognitive processes of children’s memory and knowledge development are described based on concept algebra and the object-attribute-relation (OAR) model. The design of the simulation tool for children’s knowledge acquisition and memory development is presented with the graphical representor of memory and the dynamic concept network of knowledge. Applications of the simulation tool are described by case studies on children’s knowledge acquisition about family members, relatives, and transportation. This work is a part of the development of cognitive computers that mimic human knowledge processing and autonomous learning.

scholarly journals Sistem Pakar Berbasis Web untuk Pengenalan Dini terhadap Karakter Investasi Individu

2012 ◽  
Vol 3 (1) ◽  
pp. 281
Author(s):  
Albert V. Dian Sano
Keyword(s):  
User Interface ◽  
Expert System ◽  
Early Detection ◽  
Knowledge Representation ◽  
Knowledge Acquisition ◽  
Knowledge Engineer ◽  
Web Based ◽  
Reasoning Process ◽  
Computer Based ◽  
System Knowledge

The objective of this research is to develop a web-based expert system for early detection towards characters of investment of people who will invest immediately on their productive ages. The development of this application is encompassing four primary activities in developing an expert system, namely: knowledgeacquisition, knowledge representation, knowledge inferencing, and knowledge transfering. Knowledge acquisition is a process of acquisition or transfering knowledge or expertise of an expert through a knowledge engineer. Knowledge representation is a process of transfering knowledge into a computer based system. Knowledge inferencing is a reasoning process performed by an expert system to draw a conclusion or a final result. The reasoning process applies a forward-chaining method. Knowledge transfering is a process of transfering knowledge from an expert system to a user (non-expert one) through a user interface of the expert system. This expert system has been tested to about 300 users. This simple system generally runs quite well although there are still some weakness to refine in the further research.

ADVANCES IN KNOWLEDGE ACQUISITION AND REPRESENTATION

2006 ◽  
Vol 15 (06) ◽  
pp. 867-874 ◽  
Author(s):  
LAWRENCE B. HOLDER ◽  
ZDRAVKO MARKOV ◽  
INGRID RUSSELL
Keyword(s):  
Knowledge Representation ◽  
Knowledge Acquisition ◽  
Performance Feedback ◽  
Human Interaction ◽  
Alternative Methods ◽  
Special Issue ◽  
Fundamental Challenge ◽  
End To End ◽  
Definition Of ◽  
Selection Of

The articles in this special issue represent advances in several areas of knowledge acquisition and knowledge representation. In this article we attempt to place these advances in the context of a fundamental challenge in AI; namely, the automated acquisition of knowledge from data and the representation of this knowledge to support understanding and reasoning. We observe that while this work does indeed advance the field in important areas, the need exists to integrate these components into an end-to-end system and begin to extract general methodologies for this challenge. At the heart of this integration is the need for performance feedback throughout the process to guide the selection of alternative methods, the support for human interaction in the process, and the definition of general metrics and testbeds to evaluate progress.

Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system

2004 ◽  
Vol 30 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Karl Boegl ◽  
Klaus-Peter Adlassnig ◽  
Yoichi Hayashi ◽  
Thomas E. Rothenfluh ◽  
Harald Leitich
Keyword(s):  
Knowledge Representation ◽  
Knowledge Acquisition ◽  
Medical Consultation ◽  
Consultation System ◽  
Fuzzy Knowledge

The dynamics of specialized knowledge representation

Terminology ◽  
2011 ◽  
Vol 17 (1) ◽  
pp. 9-29 ◽  
Author(s):  
Pamela Faber
Keyword(s):  
Knowledge Representation ◽  
Knowledge Acquisition ◽  
Cognitive Processing ◽  
Situation Model ◽  
Meaning Construction ◽  
Continuous Change ◽  
Specialized Knowledge ◽  
Mind And Brain ◽  
Concept Representations ◽  
The Mind

Dynamicity is the condition of being in motion, and thus, is characterized by continuous change, activity, or progress. Not surprisingly, dynamicity is generally acknowledged to be an important part of any kind of knowledge representation system or knowledge acquisition scenario. This means that it might be a good idea to reconsider concept representations in Terminology, and modify them so that they better reflect the nature of conceptualization in the mind and brain. In this sense, recent theories of cognition have emphasized that situated or grounded experiences are activated in cognitive processing (Louwerse and Jeuniaux 2010; Barsalou 1999; Zwaan 2003). According to these theories, meaning construction heavily relies on perceptually simulating the information that is presented to the comprehender. Specialized knowledge representation that facilitates knowledge acquisition could thus be conceived as a situation model or event that enables comprehenders to use communicated information to better interact with the world

A Computational Simulation of the Cognitive Process of Children Knowledge Acquisition and Memory Development

2011 ◽  
Vol 5 (2) ◽  
pp. 17-36 ◽  
Author(s):  
Jeff Bancroft ◽  
Yingxu Wang
Keyword(s):  
Knowledge Representation ◽  
Knowledge Acquisition ◽  
Computational Simulation ◽  
Simulation Tool ◽  
Memory Development ◽  
Cognitive Mechanisms ◽  
Children's Memory ◽  
Mechanisms Of Memory ◽  
The Mathematical Model ◽  
The Brain

The cognitive mechanisms of knowledge representation, memory establishment, and learning are fundamental issues in understanding the brain. A basic approach to studying these mental processes is to observe and simulate how knowledge is memorized by little children. This paper presents a simulation tool for knowledge acquisition and memory development for young children of two to five years old. The cognitive mechanisms of memory, the mathematical model of concepts and knowledge, and the fundamental elements of internal knowledge representation are explored. The cognitive processes of children’s memory and knowledge development are described based on concept algebra and the object-attribute-relation (OAR) model. The design of the simulation tool for children’s knowledge acquisition and memory development is presented with the graphical representor of memory and the dynamic concept network of knowledge. Applications of the simulation tool are described by case studies on children’s knowledge acquisition about family members, relatives, and transportation. This work is a part of the development of cognitive computers that mimic human knowledge processing and autonomous learning.

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