**Section 2**

**Representations** 

22 Will-be-set-by-IN-TECH

40 Advances in Knowledge Representation

Minker, J. (1998). An Overview of Cooperative Answering in Databases, *Proceedings of the*

Patella, M. & and Ciaccia, P. (2009). Approximate Similarity Search: A Multi-faceted Problem. *Journal of Discrete Algorithms*, Vol. 7, No. 1, March -2009, -36 – -48, ISSN 1468-0904. Peeva, K. & Kyosev, Y. (2004). *Fuzzy Relational Calculus: Theory, Applications And*

Penzo, W. (2005). Rewriting Rules to Permeate Complex Similarity and Fuzzy Queries within

Rosado, A.; Ribeiro, R. A.; Zadrozny, S. & Kacprzyk, J. (2006). Flexible Query Languages

Salii, V. N. (1983). Quasi-Boolean Lattices and Associations. In: *Lectures in Universal*

Schmitt, I. & Schulz, N. (2004). Similarity Relational Calculus and its Reduction to a

Suciu, D. (2008). Probabilistic Databases. *SIGACT News*, Vol. 39, No. 2, June 2008, -111 – -124,

Ullman, J. D. (1988). *Principles of Database and Knowledge-Base Systems*, Vol. I, Computer Science

Ullman, J. D. (1989). *Principles of Database and Knowledge-Base Systems: The New Technologies*,

Van der Meyden, R. (1998). Logical Approaches to Incomplete Information: A Survey, In:

Yazici, A. & George, R. (1999). *Fuzzy Database Modeling*, Physica-Verlag, Heidelberg, Germany. Zadeh, L. A. (1965). Fuzzy Sets. *Information and Control*, Vol. 8, No. 3, June 1965, -338 – -353,

*Logics for Databases and Information Systems*, Chomicki, J. & Saake, G. (Eds.), page numbers (307-356), Kluwer Academic Publishers, ISBN 0-7923-8129-7, Norwell,

*Universalis*, Vol. 46, No. 3, Sept 2001, -417 – -441, ISSN 0002-5240.

Vol. 17, No. 2, Feb 2005, -255 – -270, ISSN 1041-4347.

978-3-540-33288-6, Berlin Heidelberg, Germany.

Vol. 1., Cambridge University Press, Cambridge, England.

Vol. II, Computer Science Press, Inc., Rockville, USA.

9-812-56076-9.

Nerherlands.

ISSN 0163-5700.

ISSN 0019-9958.

USA.

Press, Inc., Rockville, USA.

*3rd International Conference on Flexible Query Answering Systems*, pp. 282-285, ISBN 3-540-65082-2, Roskilde, Denmark, May 1998, Springer-Verlag, Berlin Heidelberg. Montagna, F. & Sebastiani, V. (2001). Equational Fragments of Systems for Arithmetic. *Algebra*

*Software (Advances in Fuzzy Systems)*, World Scientific Publishing Company, ISBN

a Relational Database System. *IEEE Transactions on Knowledge and Data Engineering*,

for Relational Databases: An Overview, In: *Flexible Databases Supporting Imprecision and Uncertainty*, Bordogna, G. & Psaila, G. (Eds.), pp. 3-53, Springer-Verlag, ISBN

*Algebra: Proceedings of Colloquia Mathematica Societatis János Bolyai*, Vol. 43, Szabo, L. & Szendrei, A. (Eds.), pp. 429-454, North-Holland, ISBN xxx, Amsterdam, The

Similarity Algebra, In: *Lecture Notes in Computer Science: Foundations of Information and Knowledge Systems - FoIKS 2004*, Vol. 2942, Seipel, D. & Turull Torres, J. M. (Eds.), pp. 252-272, Springer-Verlag, ISBN 3-540-20965-4, Berlin Heidelberg, Germany. Shenoi, S. & Melton, A. (1989). Proximity Relations in the Fuzzy and Relational Database

Model. *Fuzzy Sets and Systems*, Vol. 31, No. 3, July 1989, -285 – -296, ISSN 0165-0114. Stanley, R. (1997). *Cambridge Studies in Advanced Mathematics 49: Enumerative Combinatorics*,

**3** 

*Mexico* 

**A General Knowledge Representation** 

*Tec of Monterrey Campus Queretaro, DASL4LTD Research Group* 

Knowledge is not a simple concept to define, and although many definitions have been given of it, only a few describe the concept with enough detail to grasp it in practical terms; knowledge is sometimes seen as a *thing* out in the real word waiting to be uncovered and taken in by the receptive mind; however, knowledge is not a *thing* to be encountered and taken in, no knowledge can be found in any mind without first have been processed by cognition. Knowledge is not something just to be uncovered or transmitted and stored, it has to be constructed. The construction of knowledge involves the use of previous knowledge and different cognitive processes, which play an intertwined function to facilitate the development of association between the new concepts to be acquired and previously acquired concepts. Knowledge is about information that can be used or applied, that is, it is information that has been contextualised in a certain domain, and therefore, any piece of knowledge is related with more knowledge in a particular and different way in each

In this chapter, a model for the representation of conceptual knowledge is presented. Knowledge can have many facets, but it is basically constituted by static components, called concepts or facts, and dynamic components, called skills, abilities, procedures, actions, etc., which together allow general cognition, including all different processes typically associated to it, such as perceiving, distinguishing, abstracting, modelling, storing, recalling, remembering, etc., which are part of three primary cognitive processes: learning, understanding and reasoning (Ramirez and Cooley, 1997). No one of those processes can live isolated or can be carried out alone, actually it can be said that those processes are part of the dynamic knowledge, and dynamic knowledge typically requires of conceptual or

In the first section of this chapter, a review of the basic concepts behind knowledge representation and the main types of knowledge representation models is presented; in the second section, a deep explanation of the components of knowledge and the way in which they are acquired is provided; in the third section, a computer model for knowledge representation called Memory Map (MM) that integrates concepts and skills is explained, and in section four, a practical application for the MM in a learning environment is

**1. Introduction** 

individual.

presented.

factual knowledge to be used.

**Model of Concepts** 

Carlos Ramirez and Benjamin Valdes
