Knowledge Graphs and Large Language Models
Current Approaches, Challenges, and Future Directions
- 1st Edition - September 1, 2026
- Latest edition
- Editors: Sanju Tiwari, Sven Groppe, Jinghua Groppe, Nandana Mihindukulasooriya
- Language: English
Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions explores the cutting-edge fusion of two powerful artificial intelligence techno… Read more
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.
- Explores integration techniques for combining LLMs and KGs
- Enhances understanding of AI applications with context and accuracy
- Provides practical insights through real-world case studies
- Addresses ethical and technical challenges in LLM-KG synergy
2. Foundations of Large Language Models. Capabilities and Challenges
3. Knowledge Graphs. Structure, Function, and Significance
4. Bridging LLMs and KGs. A Conceptual Framework
5. Techniques for Integrating LLMs and KGs
6. Enhancing Knowledge Graphs With Large Language Models
7. Improving Language Models With Knowledge Graph Insights
8. Applications of LLM–KG Synergy Across Domains
9. Ethical, Social, and Technical Challenges in LLM–KG Integration
10. GOSt-MT. A Knowledge Graph for Occupation-Related Gender Biases in Machine Translation
11. Future Innovations in Combining LLMs and KGs
12. Conclusion. Advancing the Frontiers of Intelligent Systems
- Edition: 1
- Latest edition
- Published: September 1, 2026
- Language: English
ST
Sanju Tiwari
SG
Sven Groppe
JG
Jinghua Groppe
Jinghua Groppe is a postdoctoral fellow at the University of Lübeck, Germany. She has been involved in a large number of research projects in Artificial Intelligence, Quantum Computing, Semantic Web, Information Technology and other areas of Computer Science. She has extensive research experience and has published over sixty papers in peer-reviewed journals, conferences and workshops. She serves as a reviewer for the German Research Foundation (DFG) and for a large number of journals and conferences, and also co-organizes conferences, workshops and special issues for journals Her current research areas include generative AI, LLMs, graph neural networks, knowledge graphs, quantum computing, and the application of artificial intelligence in cybersecurity and in the automation of business processes. In addition to her research projects, she also works with industry on technology transfer projects and applies AI to automated business processes.
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Nandana Mihindukulasooriya
Nandana Mihindukulasooriya is a Senior Research Scientist at IBM Research, New York, USA. He holds a PhD in Artificial Intelligence from Universidad Politecnica de Madrid, Spain. His research interests include knowledge graphs, knowledge representation and reasoning, Semantic Web, Linked Data, and generative AI. Nandana has published more than 85 peer-reviewed papers in prestigious journals and conferences on Semantic Web and Knowledge Graph related topics with an h-index of 21. He has contributed as a PC member to several conferences, including WWW, AAAI, ACL, EMNLP, IJCAI, ISWC, ESWC, SAC, and K-CAP, among others. Nandana has previously co-organized several workshops, including NLP4KGC 2023-2024 @ WWW/ TheWebConf and SEMANTiCS, Text2KG 2022-2024 at Extended Semantic Web Conference (ESWC), SMART 2020-2022, ScholarlyQALD 2023, KGSum 2022 at International Semantic Web Conference (ISWC), ToursimKG 2018 @ ICWE. In addition, Nandana has been the general chair, publicity chair, and sponsorship chair of IHIC 2022, ISIC 2023, and ISWC 2024.