The rise of Large Language Models (LLMs) has revolutionized the best way we extract information from text and interact with it. Nonetheless, despite their impressive capabilities, LLMs face several inherent challenges, particularly in areas similar to reasoning, consistency, and data’s contextual accuracy. These difficulties come from the probabilistic nature of LLMs, which may result in hallucinations, lack of transparency, and challenges in handling structured data.
That is where Knowledge Graphs (KGs) come into play. By integrating LLMs with KGs, AI-generated knowledge will be significantly enhanced. Why? KGs provide a structured and interconnected representation of data, reflecting the relationships and entities in the actual world. Unlike traditional databases, KGs can capture and reason in regards to the complexities of human knowledge, ensuring that the outputs of LLMs come from a structured, verifiable knowledge base. This integration results in more accurate, consistent, and contextually relevant outcomes.
Industries like healthcare, finance, and legal services can greatly profit from knowledge graphs resulting from their need for precise and…
