1 Knowledge graphs and LLMs: a killing combination

 

This chapter covers

  • Introduction to Generative AI and, in particular, Large Language Models (LLMs)
  • The four pillars of knowledge graphs (KGs)
  • How KGs and LLMs can be combined
  • The application of knowledge graphs in multiple domains

In recent years, artificial intelligence (AI) has contributed to reaching unprecedented advances in many areas, helping to develop a new generation of systems to support decision-making and several other complex applications. New Generative AI [1] technologies – with their capability to “generate” text, images, and videos – are making new waves in diverse business areas. For example, ChatGPT, a chatbot that interacts with users through a web interface, was made publicly accessible on November 30, 2022, by the San Francisco company called OpenAI [2] . Users can have a conversation with it, ask it to write code, summarize a long piece of text, and do many other similar tasks. In a few days, it reached 1 million users, and in a few months more than 100 million. It was the beginning of a new era. On the wave of this success, other companies made similar generative AI tools accessible and easy to use to a broader public – outside of the small circle of Machine Learning practitioners and Data Scientists – extending enormously the user base and the type of 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 application

 

1.4.1 The four pillars of knowledge graphs

 
 

1.5 Building data-driven applications using KGs and LLMs

 
 
 

1.5.1 Customer 360-degrees for financial services

 
 

1.5.2 Drug discovery and development

 
 
 

1.5.3 Conversational AI for customers support

 
 
 

1.5.4 Recommendation system in retail

 
 

1.5.5 What should I ask myself?

 
 

1.6 Knowledge graph technologies

 
 
 
 

1.7 Making graphs smarter using semantics

 
 

1.7.1 Graph vs. knowledge graph

 
 
 

1.7.2 Taxonomies and ontologies

 
 
 
 

1.8 How do we teach knowledge graphs and LLMs?

 
 
 
 

1.9 Summary

 
 
 

1.10 References

 
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