Knowledge-based

Knowledge-based, in essence, describes systems, processes, or entities that rely heavily on acquired information, facts, and understanding to function, make decisions, or solve problems. This contrasts with approaches that are primarily data-driven or algorithmic without a strong foundation of existing knowledge. It emphasizes the importance of expertise, learned principles, and accumulated wisdom in guiding actions and generating outputs. knowledge-based systems often incorporate sophisticated mechanisms for storing, retrieving, and reasoning with information, aiming to emulate human-like cognitive abilities. It can refer to processes, programs, and entire organizations, demonstrating a deep dependence on structured and unstructured information to make optimal decisions.

Knowledge-based meaning with examples

• The company implemented a knowledge-based system to improve customer service. This system utilizes a database of FAQs, past interactions, and expert knowledge to provide personalized and efficient responses to inquiries, thereby boosting customer satisfaction, and reducing the need for human intervention. The system is intended to give accurate and expedient information to customers.
• Doctors use knowledge-based diagnostic tools. These applications contain extensive medical data, and provide insight for different conditions, treatments, and test results. This aids in more accurate and timely diagnosis and treatment plans. This relies on having a large database of information available.
• A knowledge-based approach is used in AI for game playing. The artificial intelligence is fed an understanding of rules and strategies. The AI models itself as a highly complex entity that contains knowledge, and builds strategies. This ensures the development of adaptive and proficient game-playing agents.
• Expert systems within engineering often operate on a knowledge-based foundation. This facilitates design optimization, troubleshooting, and failure analysis. These systems combine engineering principles, empirical data, and industry best practices. This allows for rapid evaluation and implementation of designs.
• In financial modeling, knowledge-based systems enhance risk assessment. They integrate macroeconomic trends, market data, and expert judgment to predict potential market downturns and provide recommendations for portfolio diversification. This leads to better informed investment decisions.