Overview

In the age of artificial intelligence, where vast oceans of data are generated every second, the ability to extract meaningful insights, reason over complex relationships, and deliver contextually accurate responses has become the defining challenge of our time. Large language models (LLMs) such as GPT-4, Claude, and Grok have revolutionized natural language understanding and generation, enabling machines to converse fluently, summarize documents, write code, and even compose poetry. Yet, beneath their impressive fluency lies a critical limitation: these models are fundamentally statistical engines trained on patterns in text, not on structured, verifiable truth. They excel at prediction but falter at consistency, often producing plausible-sounding but factually incorrect statements-a phenomenon known as hallucination. This gap between linguistic proficiency and factual reliability has created an urgent need for a new paradigm in AI architecture, one that marries the expressive power of neural networks with the logical rigor of symbolic reasoning. At the center of this paradigm shift stands the knowledge graph.

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