Part V: Information retrieval with knowledge graphs and LLMs

 

The integration of Knowledge Graphs with Large Language Models reaches its practical culmination in this final part, where we explore how to effectively leverage these combined technologies for accurate and reliable information retrieval. While previous parts established the foundations and demonstrated various construction and enrichment techniques, these closing chapters focus on the practical implementation of systems that use knowledge graphs as ground truth to enhance LLM capabilities while preventing hallucinations.

The synergy between KGs and LLMs creates a powerful framework where the structured, verifiable nature of knowledge graphs provides the factual backbone that constrains and guides the natural language understanding and generation capabilities of LLMs. This combination enables the development of systems that can:

  • Provide accurate, knowledge-grounded responses to complex queries
  • Transform natural language questions into precise graph queries
  • Maintain contextual awareness across multiple interactions
  • Deliver explainable results through transparent reasoning paths
  • Scale effectively while preserving accuracy and reliability