1 Knowledge graphs and LLMs: A killer combination

Generative artificial intelligence (GenAI), powered by large language models (LLMs) like Google’s Gemini and OpenAI’s GPT, has transformed how we work and live, revolutionizing business after business. Despite this success, generative AI falls short in domains where specific domain knowledge, high accuracy, and explainability are essential. And it has other significant limitations, including hallucinations and a lack of context and relations. This is where knowledge graphs (KGs) come in, providing contextual information—such as experiences, environmental characteristics, cultural aspects, and social norms—needed to build the “third wave of AI” [1] for mission-critical applications.

1.1 Knowledge graphs

1.2 Large language models

1.3 KGs and LLMs: Stronger together

1.4 The paradigm shift in data-driven applications

1.4.1 The four pillars of knowledge graphs

1.5 Building data-driven applications using KGs and LLMs

1.5.1 Example use case: Drug discovery and development

1.5.2 Example use case: Conversational AI for customer support

1.5.3 Deciding whether to use a KG

1.6 Knowledge graph technologies

1.6.1 Taxonomies and ontologies

1.7 How do we teach KGs and LLMs?

Summary