Part I: Foundations of hybrid intelligent systems
The convergence of Knowledge Graphs (KGs) and Large Language Models (LLMs) marks a pivotal moment in the development of intelligent systems. This part lays the theoretical and practical foundation for understanding how these complementary technologies can work together to create more powerful and effective solutions. While KGs have long provided a robust framework for representing structured knowledge and supporting explicit reasoning, and LLMs have recently revolutionized natural language understanding and generation, their combination opens new possibilities that were unattainable just a year ago.
The integration of these technologies addresses the key limitations of each approach while amplifying their strengths. KGs provide the explicit, verifiable, and updatable knowledge representation that LLMs often lack, while LLMs offer the natural language understanding and generation capabilities that make complex knowledge structures more accessible. This synergy enables the development of intelligent systems that can:
- Handle both structured and unstructured data effectively
- Combine multiple types of reasoning strategies
- Provide explainable and verifiable results
- Continuously update their knowledge base
- Interact naturally with users while maintaining accuracy and reliability