Large language models (LLMs) and knowledge graphs (KGs) are complementary technologies that balance each other's strengths and weaknesses when combined: - LLMs have a strong capability for understanding and generating natural language, but can sometimes hallucinate facts. - KGs explicitly represent factual knowledge in a structured format, but lack language understanding. - Together, LLMs can provide context and nuance to the rigid facts in KGs, while KGs can ground the free-flowing text from LLMs in reality. - The intuitive knowledge in LLMs complements the logical knowledge in KGs. KGs can enhance LLMs with external knowledge to improve reasoning and reliability. LLMs can help KGs better utilize textual data. - By synergizing, LLMs and KGs can achieve enhanced performance on language and knowledge intensive tasks compared to using either technology alone. Their partnership enables fulfilling AI's promise to augment human intelligence through a fusion of data-driven and knowledge-driven approaches. In summary, the complementary strengths of large neural language models and structured knowledge graphs make them ideal partners for AI systems aiming to combine reasoning, language, and knowledge capabilities. Their synergistic unification can overcome limitations of both approaches.