Xingming Liao
Papers
1
Total Citations
58
H-Index
1
About
Xingming Liao is a leading researcher at the intersection of artificial intelligence, knowledge engineering, and industrial automation. His primary research areas include large language models (LLMs), knowledge graph construction, and intelligent fault diagnosis for robotic systems. Liao’s most notable contribution is his pioneering work on leveraging LLMs to automate the creation of fine-grained knowledge graphs, a breakthrough that significantly enhances the efficiency and accuracy of fault diagnosis in complex robotic environments. His highly cited 2025 paper, "Large language model assisted fine-grained knowledge graph construction for robotic fault diagnosis," has already garnered 58 citations, reflecting its immediate impact on both academia and industry. This work addresses a critical challenge in smart manufacturing by enabling machines to reason about failures with unprecedented precision, reducing downtime and maintenance costs. Liao’s research bridges the gap between cutting-edge AI and practical engineering applications, offering scalable solutions for next-generation autonomous systems. His achievements position him as a key figure in advancing explainable and reliable AI for industrial robotics.
Research Focus
Key Achievements
Top Papers
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