Knowledge Representation and Relation Nets |
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| Author:
| Geldenhuys, Aletta E.
van Rooyen, Hendrik O.
Stetter, Franz |
| Series title: | The Springer International Series in Engineering and Computer Science Ser. |
| ISBN: | 978-1-4613-6815-1 |
| Publication Date: | Oct 2012 |
| Publisher: | Springer London, Limited
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| Book Format: | Paperback |
| List Price: | AUD $383.95 |
Book Description:
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Knowledge Representation and Relation Nets introduces a fresh approach to knowledge representation that can be used to organize study material in a convenient, teachable and learnable form. The method extends and formalizes concept mapping by developing knowledge representation as a structure of concepts and the relationships among them. Such a formal description of analogy results in a controlled method of modeling `new' knowledge in terms of `existing' knowledge in teaching and...
More DescriptionKnowledge Representation and Relation Nets introduces a fresh approach to knowledge representation that can be used to organize study material in a convenient, teachable and learnable form. The method extends and formalizes concept mapping by developing knowledge representation as a structure of concepts and the relationships among them. Such a formal description of analogy results in a controlled method of modeling `new' knowledge in terms of `existing' knowledge in teaching and learning situations, and its applications result in a consistent and well-organized approach to problem solving. Additionally, strategies for the presentation of study material to learners arise naturally in this representation. While the theory of relation nets is dealt with in detail in part of this book, the reader need not master the formal mathematics in order to apply the theory to this method of knowledge representation. To assist the reader, each chapter starts with a brief summary, and the main ideas are illustrated by examples. The reader is also given an intuitive view of the formal notions used in the applications by means of diagrams, informal descriptions, and simple sets of construction rules. Knowledge Representation and Relation Nets is an excellent source for teachers, courseware designers and researchers in knowledge representation, cognitive science, theories of learning, the psychology of education, and structural modeling.