Design and Implementation of a Knowledge Base for Machine Knowledge Learning

Abstract

Knowledge bases are a fundamental platform for knowledge acquisition, retaining, retrieval, reasoning and generation across machine learning, natural language processing and computational intelligence. The mathematical model of formal concepts is centric in knowledge bases for modeling the basic unit of human knowledge and thinking threads. This paper presents the design and implementation of a cognitive knowledge base. The structure of the knowledge base is created as a dynamic concept network mimicking human knowledge represented in the brain. The knowledge base enables a set of novel knowledge manipulations for machine learning such as knowledge acquisition, access, analysis, refinement, fusion and system maintenance. Experimental results demonstrate the performance and efficiency of the implementation of the generic knowledge base for cognitive robots and machine learning systems.