IThe rise of large language models (LLMs) has revolutionized the way we extract information from and interact with text. However, despite their impressive capabilities, LLMs face several inherent challenges, particularly in areas such as reasoning, consistency, and contextual accuracy of information. These difficulties stem from the probabilistic nature of LLMs, which can lead to hallucinations, lack of transparency, and challenges in handling structured data.
This is where knowledge graphs (KGs) come into play. By integrating LLMs with KGs, ai-generated knowledge can be significantly improved. Why? KGs provide a structured and interconnected representation of information, reflecting real-world relationships and entities. Unlike traditional databases, KGs can capture and reason about the complexities of human knowledge, ensuring that LLM outputs come from a structured and verifiable knowledge base. This integration leads to more accurate, consistent, and contextually relevant results.
Industries such as healthcare, finance, and legal services can greatly benefit from knowledge graphs due to their need for…
