Knowledgeinfused

Knowledge-infused refers to the process of integrating knowledge, often from various sources, into a system, model, or application to enhance its performance, accuracy, or functionality. This concept is widely used in artificial intelligence, machine learning, and data science. Knowledge-infused systems leverage domain-specific knowledge to improve their understanding and decision-making capabilities. This knowledge can be derived from expert systems, ontologies, databases, or other structured sources. By incorporating this knowledge, systems can better interpret complex data, make more informed predictions, and provide more accurate recommendations. The process of knowledge infusion typically involves knowledge acquisition, representation, and integration. Knowledge acquisition involves gathering relevant information from various sources, while representation involves structuring this information in a way that can be easily understood and utilized by the system. Integration then involves embedding this knowledge into the system's architecture, ensuring it is accessible and usable during the system's operation. Knowledge-infused systems have been successfully applied in various fields, including healthcare, finance, and natural language processing, demonstrating their potential to significantly improve the performance of intelligent systems.