Liting Jing
Papers
4
Total Citations
112
H-Index
3
About
Dr. Liting Jing is a rising scholar at the forefront of intelligent product design and innovation, whose work bridges the gap between unstructured knowledge and structured decision-making. Her research centers on three key areas: patent text mining, knowledge graph construction, and multi-criteria decision-making under uncertainty. Dr. Jing’s major contributions include pioneering a patent text-based conceptual design decision-making approach that fuses incomplete evaluation semantics with scheme beliefs—a method that has already garnered 64 citations since its 2024 publication. She further advanced the field by developing a product innovation design approach driven by implicit relationship completion via patent knowledge graphs (37 citations), enabling designers to uncover hidden connections between disparate technological domains. Her work on concept evaluation under incomplete information, considering large-scale criteria and risk attitudes, provides a robust framework for handling real-world design complexity. Most recently, Dr. Jing has introduced a knowledge graph-assisted design-by-analogy method that structures analogical knowledge retrieval, reducing reliance on designer intuition. With a growing citation impact and a clear trajectory toward AI-enhanced creativity, Dr. Jing is establishing herself as a key innovator in computational design methodology.
Research Focus
Key Achievements
Top Papers
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