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Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions (en Inglés)
Tiwari, Sanju; Groppe, Sven; Groppe, Jinghua (Autor)
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Academic Press
· Tapa Blanda
Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions (en Inglés) - Tiwari, Sanju; Groppe, Sven; Groppe, Jinghua
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Reseña del libro "Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions (en Inglés)"
Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions explores the cutting-edge fusion of two powerful artificial intelligence technologies: Large Language Models (LLMs) and Knowledge Graphs (KGs). LLMs have revolutionized our daily lives with their ability to generate human-like text, perform text classification, translate languages, summarize content, and even code. Meanwhile, KGs have structured information for decades, enabling machine-processable data and inference between real-world entities. This book addresses the growing interest in combining LLMs and KGs to leverage their respective strengths, enhancing machines' ability to process, interpret, and generate information with context and accuracy. The book is structured to provide a comprehensive understanding of this emerging field. Chapter 1 introduces the synergy between LLMs and KGs, setting the stage for the subsequent chapters. Chapter 2 delves into the capabilities and challenges of LLMs, while Chapter 3 focuses on the structure, function, and significance of KGs. Chapter 4 presents a conceptual framework for bridging LLMs and KGs, followed by Chapter 5, which discusses techniques for their integration. Chapters 6 and 7 explore how LLMs can enhance KGs and vice versa. Chapter 8 showcases applications of LLM-KG synergy across various domains. Chapter 9 addresses ethical, social, and technical challenges, and Chapter 10 looks ahead to future innovations. The book concludes with Chapter 11, summarizing key insights and advancements in intelligent systems. Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions is an essential resource for graduate students, researchers, and professionals in computer science. It offers valuable insights for adopting LLMs, KGs, and their advanced applications in research and product development. By bridging the gap between these technologies, this book equips readers with the knowledge to drive innovation and enhance the capabilities of intelligent systems.
