Daeyoung Park
Papers
1
Total Citations
58
H-Index
1
About
Daeyoung Park is a leading researcher at the intersection of artificial intelligence, human-robot interaction, and knowledge engineering. His most cited work, "Merged Ontology and SVM-Based Information Extraction and Recommendation System for Social Robots" (2017, 58 citations), tackles a critical challenge in social robotics: enabling robots to understand natural language queries and deliver personalized recommendations. By fusing ontology-based knowledge representation with Support Vector Machine (SVM) learning, Park developed a system that allows humanoid robots to extract relevant information from spoken commands and provide context-aware suggestions. This contribution bridges the gap between raw voice data and intelligent decision-making, enhancing the autonomy and usefulness of social robots in real-world settings. Park's research has been instrumental in advancing how machines interpret human speech and structure knowledge for interactive tasks. His work is widely cited in robotics and AI communities, reflecting its impact on developing more responsive, conversational agents. Through his innovative integration of machine learning and semantic technologies, Daeyoung Park continues to shape the future of socially intelligent robots.
Research Focus
Key Achievements
Top Papers
- 1