Semantic Knowledge Management Through Cognitive Automation Using NLP and Ontological Reasoning for Contextual Semantic Inference and Intelligent Knowledge

Abstract

NLP and cognitive reasoning are combined to fulfill the increasing requirements of intelligent and explainable knowledge management. The goal is to create an adaptive framework, which would be able to conduct contextual semantic inference and intelligent knowledge discovery. The difference is in the fact that transformer-based embeddings are combined with ontology-driven reasoning and reinforcement learning to adapt to dynamic context. The suggested Cognitive Ontology-Integrated Neuro-Semantic Automation Framework represents rich linguistic encodings, integrates them with ontological structures, and combines neural and symbolic reasoning over them in a Neuro-Symbolic Fusion Network. The Explainable Knowledge Visualizer makes things easier to understand by creating semantic maps of inferred relations. A test of the DBpedia dataset through experimental analysis shows an accuracy of the framework of 98.68 which is very efficient in semantic extraction, contextual reasoning and clear visualization of knowledge.