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Abstract
Abstract: This article discusses models for representing knowledge in artificial intelligence, offering new perspectives, logical inferences, knowledge representation languages, and domains of reasoning. Overall, it emphasizes the need to take a different view when forming a knowledge base. It argues that results can be inferred using networked models, which can further advance intelligent systems. Evaluating events and states in real-world processes by considering every detail helps one approach situations correctly, meeting the demands of the time, and thus forms an important foundation in human life.
References
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