Yali Qiao

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About

Yali Qiao is a researcher whose work lies at the intersection of patent informatics, natural language processing, and semantic analysis. Her most notable contribution is the development of a novel method for measuring patent similarity using Subject-Action-Object (SAO) semantic structures, as detailed in her highly cited 2019 paper "Measuring patent similarity with SAO semantic analysis," which has garnered 60 citations. This work introduced a more nuanced approach to comparing patents by moving beyond simple keyword matching to capture the functional relationships between technical components, offering significant improvements in accuracy for patent retrieval, classification, and competitive intelligence. Qiao’s research has direct implications for innovation management, helping analysts and organizations better map technological landscapes and identify potential areas for collaboration or infringement. Her contributions stand out for bridging computational linguistics with intellectual property analysis, providing a practical tool that has been adopted in subsequent studies on patent mining and technology forecasting. Through this work, Yali Qiao has established herself as a key voice in advancing semantic methods for patent analysis, making complex technical documents more accessible and actionable for researchers and practitioners alike.