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Knowledge Distillation through a Knowledge Representation Approach (Knowledge Engineering)

Authors :

Mohammad Hadi Safari Nader¹

1- دانشگاه فردوسی مشهد

Keywords :

Knowledge Distillation،Knowledge Engineering،Knowledge Representation،Model Compression،Machine Learning

Abstract :

Knowledge distillation, as an advanced technique in machine learning, enables the compression of complex and large-scale models into smaller and more efficient ones. This technique, commonly employed to reduce computational costs and increase execution speed, has gained particular importance in systems based on reasoning and knowledge representation. Knowledge engineering, as the process of constructing, storing, and analyzing knowledge data, plays a crucial role in enhancing decision-making and reasoning processes in intelligent systems . This paper analyzes the interrelation between knowledge distillation and knowledge engineering, particularly within the contexts of knowledge representation and reasoning. By examining the challenges and opportunities arising from the application of knowledge distillation in these areas, the study highlights its significance in improving the efficiency of knowledge engineering–based systems and proposes strategies for effectively integrating these techniques into intelligent systems

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