Knowledge Graph Question Answering and Large Language Models
Research output: Contributions to collected editions/works › Chapter
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Handbook on Neurosymbolic AI and Knowledge Graphs. ed. / Pascal Hitzler; Abhilekha Dalal; Mohammad Saeid Mahdavinejad; Sanaz Saki Norouzi. IOS Press BV, 2025. p. 466-531 ( Frontiers in Artificial Intelligence and Applications; Vol. 400).
Research output: Contributions to collected editions/works › Chapter
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TY - CHAP
T1 - Knowledge Graph Question Answering and Large Language Models
AU - Banerjee, Debayan
AU - Hu, Nan
AU - Tan, Yimin
AU - Min, Dehai
AU - Wu, Yike
AU - Usbeck, Ricardo
AU - Qi, Guilin
PY - 2025/3/17
Y1 - 2025/3/17
N2 - Knowledge Graph Question Answering (KGQA) is an evolving field that aims to leverage structured knowledge graphs to provide precise answers to user queries. As Knowledge Graphs continue to expand in complexity and size, efficiently navigating and extracting relevant information from these vast datasets has become increasingly challenging. Recent advancements in Large Language Models (LLMs), offer promising capabilities in understanding and processing natural language. By integrating LLMs with KGQA systems, it is possible to enhance the accuracy and contextual relevance of answers generated. In this chapter, we explore the intersection of KGQA and LLMs, evaluating their combined potential to fetch information from knowledge graphs
AB - Knowledge Graph Question Answering (KGQA) is an evolving field that aims to leverage structured knowledge graphs to provide precise answers to user queries. As Knowledge Graphs continue to expand in complexity and size, efficiently navigating and extracting relevant information from these vast datasets has become increasingly challenging. Recent advancements in Large Language Models (LLMs), offer promising capabilities in understanding and processing natural language. By integrating LLMs with KGQA systems, it is possible to enhance the accuracy and contextual relevance of answers generated. In this chapter, we explore the intersection of KGQA and LLMs, evaluating their combined potential to fetch information from knowledge graphs
KW - Informatics
KW - Business informatics
U2 - 10.3233/FAIA250220
DO - 10.3233/FAIA250220
M3 - Chapter
SN - 978-1-64368-578-6
T3 - Frontiers in Artificial Intelligence and Applications
SP - 466
EP - 531
BT - Handbook on Neurosymbolic AI and Knowledge Graphs
A2 - Hitzler, Pascal
A2 - Dalal, Abhilekha
A2 - Mahdavinejad, Mohammad Saeid
A2 - Norouzi, Sanaz Saki
PB - IOS Press BV
ER -