Knowledge-Based System


A Knowledge-Based System (KBS) is a computer program that uses artificial intelligence to solve complex problems within a specialized domain that normally requires human expertise. It uses a knowledge base, consisting of accumulated experience and expertise, to make decisions. These systems are widely used in areas like medical diagnosis, financial systems, voice recognition, robotics, and more.


The phonetics of “Knowledge-Based System” can be transcribed as: /ˈnɒlɪdʒ beɪst ˈsɪstəm/

Key Takeaways

Knowledge-Based Systems Work Through Stored Information: They heavily rely on a well-organized and extensive database, often referred to as the ‘knowledge base’. This system primarily uses the information from the database to interpret, predict, and make decisions.Expertise Mimicking: Knowledge-based systems are designed to mimic human expertise in a particular field, making decisions and providing solutions as a human expert would. They can be used in a wide variety of sectors including healthcare, finance, engineering, and more.

Components of Knowledge-Based System: There are two main components of a knowledge-based system – the knowledge base and the inference engine. The knowledge base stores the information and the inference engine uses that information to respond to various queries or solve complex problems.


Knowledge-Based Systems (KBS) represent a critical domain in technology, highlighting the integration of artificial intelligence and human expertise. The importance of KBS lies in its capability to simulate human decision-making process and problem-solving skills, thus automating complex tasks that otherwise would require human expertise. The system stores expert knowledge and applies logical rules and algorithms to deliver reliable and consistent solutions.

It’s a technology that enables drastic improvements to efficiency, reduces errors and supports decision-making. In a wider context, KBS plays a key role in various industries ranging from healthcare, finance, to manufacturing, promoting the advancement of a technology-driven society and pushing the boundaries of innovation.


A Knowledge-Based System (KBS) is a computer program designed to simulate the problem-solving abilities of a human expert in a specific domain of knowledge. Its primary purpose is to assist in decision-making processes and to simplify complex problem-solving tasks. The system utilizes a vast quantity of data, rules, and procedures, referred to as its ‘knowledge base’, and a set of procedures and rules, known as its ‘inference engine’, to process and apply this knowledge. The capability of KBS to offer expert opinions on diverse domains makes it highly advantageous in areas where a wealth of knowledge is essential but human experts might not be readily accessible.

Knowledge-Based Systems are extensively utilized across a range of sectors, including healthcare, finance, engineering, and more. For instance, in medicine, KBSs can help in more precise diagnosis by analyzing the patient’s symptoms in comparison to the extensive knowledge base of medical data. In the financial sector, these systems could assist in predicting market trends based on historical data for better investment strategies.

Likewise, in engineering, they can be programmed to offer sophisticated solutions to complex design problems. It’s their ability to gather, process, and apply vast amounts of knowledge with precision and speed that sets KBSs aside in the technological world.


1. Medical Diagnosis Systems: One of the most effective uses of Knowledge-Based Systems is found in the medical field. Systems such as MYCIN or CADUCEUS are updated with the latest diagnosis-related knowledge and are able to assist doctors by providing different potential diagnoses for a set of symptoms. They can even recommend treatments based on the conditions and past medical history of patients.

2. Financial Systems: Banks and financial institutions apply Knowledge-Based Systems for various purposes such as fraud detection, understanding credit risk, or managing financial portfolios. These systems typically have extensive knowledge about financial products, market conditions, and economic parameters and can provide financial advice or alerts based on this information.

3. Voice-activated personal assistants: Siri, Alexa, and Google Assistant are prime examples of Knowledge-Based Systems. They use databases full of information to answer various queries asked by the user such as weather updates, traffic conditions, general knowledge questions, etc. They learn from past interactions and can even perform tasks like setting up reminders, playing songs, making calls, among other things based on knowledge input from the user.

Frequently Asked Questions(FAQ)

Q1: What is a Knowledge-Based System?

A: A Knowledge-Based System (KBS) is a computer system used in problem-solving which utilizes AI procedures and relies on a base of knowledge about a particular subject or field.

Q2: How do Knowledge-Based Systems work?

A: Knowledge-Based Systems work by utilising an extensive knowledge base to make decisions or provide explanations. They use AI technologies such as machine learning and natural language processing to interpret and learn from the data they’re provided.

Q3: What are some examples of Knowledge-Based Systems?

A: Examples of Knowledge-Based Systems include medical diagnosis systems, financial systems, voice recognition systems, and even recommendation systems used by eCommerce platforms.

Q4: What are the benefits of using a Knowledge-Based System?

A: Knowledge-Based Systems can automate complex decision-making processes, improve efficiency, reduce errors, offer consistent output, and they can work around the clock.

Q5: Are there any drawbacks to Knowledge-Based Systems?

A: While powerful, KBS also have limitations. They depend heavily on the quality of the data in the system’s knowledge base. Limited, outdated, or incorrect data can lead to incorrect conclusions. They also require regular updates to keep information current, and this must usually be performed by experts.

Q6: How is a Knowledge-Based System different from a Database System?

A: While both systems store and manage data, a Database System is designed for data storage, retrieval, and management, whereas a Knowledge-Based System is designed to make decisions and provide solutions by drawing on a large amount of field-specific knowledge.

Q7: Do Knowledge-Based Systems learn and adapt over time?

A: Yes, Knowledge-Based Systems can adapt over time using AI-driven technologies like machine learning. They can learn from new data, improve their decision-making, and enhance their knowledge base over time.

Q8: Who primarily uses Knowledge-Based Systems?

A: Knowledge-Based Systems are used across a wide range of industries, including healthcare, finance, education, manufacturing, and more. Any field that requires expert decision making and interpretation can benefit from a Knowledge-Based System.

Q9: Are Knowledge-Based Systems expensive to implement?

A: The cost of implementing a Knowledge-Based System can vary widely, depending on its complexity, the industry it’s being used in, and the level of customization required.

Q10: How secure are Knowledge-Based Systems?

A: Like any other system, Knowledge-Based Systems’ security depends largely on the security measures put in place to protect them. However, they are prone to the same risks as any computer system, including data breaches and hacking.

Related Tech Terms

    • Artificial Intelligence

    • Expert System
    • Inference Engine
    • Knowledge Base

  • Machine Learning

Sources for More Information


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