Hybrid Systems for Knowledge Representation in Artificial Intelligence

TL;DR

This paper explores hybrid knowledge representation schemes in AI, aiming to improve efficiency and organization of large knowledge bases for better inference, highlighting the need for standardized methods.

Contribution

It provides an extensive analysis of various hybrid KR schemes, emphasizing their potential and challenges in constructing effective AI systems.

Findings

Hybrid KR schemes can enhance knowledge organization and inference efficiency.

Combining multiple KR methods offers benefits but also introduces complexity.

Standardized approaches are necessary for broader adoption of hybrid KR techniques.

Abstract

There are few knowledge representation (KR) techniques available for efficiently representing knowledge. However, with the increase in complexity, better methods are needed. Some researchers came up with hybrid mechanisms by combining two or more methods. In an effort to construct an intelligent computer system, a primary consideration is to represent large amounts of knowledge in a way that allows effective use and efficiently organizing information to facilitate making the recommended inferences. There are merits and demerits of combinations, and standardized method of KR is needed. In this paper, various hybrid schemes of KR were explored at length and details presented.