Towards MORK: Model for Representing Knowledge

Abstract

Abstract-Smart world needs intelligent system for effective and timely decision making. This is achieved only through a knowledge based system with functional knowledge representation units. In this paper, two models are proposed for representing knowledge. This process involves in getting the data and placing the information in the correct location. Logical notations are used for taking the clauses and graph is used for putting the entities. In Model one, the data is translated into logical statements using predicate logics, later the knowledge is stored in conceptual graph and retrieved. Whereas in Model two, the given information is translated using First Order Logic (FOL), by applying description logic concept rules are defined and as a result reasoning is done. Storage is done by using concept-relation graph. The main aims of our models are to have easy and simple access over the information. These models return the required exact answer, for the higher order query posted by the end user to the intelligent system.

A Knowledge Representation Model using Concept-Relation Graph

“…In this Model, the knowledge represented is done by converting the statement into description logic using first order logic [4]. Rules are generated and suitable reasoning is done by applying conceptual graphs structure.…”

Concept Relation Knowledge Visualization with CR Logic using Neo4j

“…Knowledge Visualization is a field of study that examines the intensity of visual structure to place knowledge. It targets supporting cognitive procedures in creating, representing, organizing retrieving sharing and utilizing knowledge [5].…”