A Knowledge-Based System (KBS) is a computer program that leverages a centralized information repository—a knowledge base—to support decision-making. A form of artificial intelligence (AI), KBSes are designed to capture knowledge from human experts and use it to inform decisions and help solve problems, much as a team of human experts might do.
KBSes are designed to simulate the decision-making abilities of a human expert within a specific domain. These expert systems rely on a reasoning system to derive new knowledge, making them a valuable tool for solving complex problems across a wide range of applications, from software development to healthcare to finance to industrial design.
In this article, we’ll cover the basics of knowledge-based systems: their essential components, their benefits and challenges, how to ensure their health and quality, and what the future might hold for knowledge-based systems.
Brief summary
Exploring the depths of knowledge-based systems
Knowledge-based systems are a crucial part of knowledge representation within AI. Knowledge representation refers to the creation of structures, processes, and models that represent information in a format the AI can use to help solve complex problems. One example is a production rule system: a precondition or “if” statement and an action or “then” statement.
Components of a knowledge-based system
Two main components make up a KBS: the knowledge base and the inference engine.
The knowledge base
A knowledge base is a centralized repository of data specific to a given field. This could be anything from medical data to hardware specifications to your company’s internal wiki. The knowledge base works in tandem with the inference engine, providing the raw data that the inference engine uses to draw conclusions and make recommendations.
The inference engine
Much like a search engine, the inference engine processes and locates data based on your requests. It employs a reasoning system to draw conclusions from the data provided and make decisions based on if-then rules, logic programming, or constraint handling rules. The inference engine applies logical reasoning to your knowledge base to expose new insights.
Why a strong user interface is important
For knowledge-based systems to succeed, they require a good user interface. It’s important to keep user requirements in mind if you want people to use your knowledge-based system, which of course you do. The user interface should make it easy for people to capture, preserve, update, and share knowledge without interrupting their established workflows. (For more tips on how to integrate AI tools into your technical workflows, read this article.)
Benefits of knowledge-based systems
Knowledge-based systems deliver obvious benefits, from better data-driven decision-making to greater efficiency and faster upskilling. When drawing from a healthy knowledge base, KBSes can:
- Serve as expert systems, to help guide expert decision-making when human experts aren’t available
- Create efficient documentation that users can access quickly and easily
- Reveal new insights from existing data, including unstructured and big data
- Organize data by analyzing and classifying information in different formats
- Handle large volumes of both structured and unstructured data
Challenges of knowledge-based systems
KBSes also come with challenges. They can be difficult to maintain, in need of continuous updating as business and technical processes evolve. They require a huge amount of high-quality data. New users often require training to understand how the system works, how to maintain it, and how to take full advantage of its capabilities. Anomalies like circular dependencies and repetitive rules can crop up in some systems. But the biggest challenge is that a knowledge-based system is only as good as the knowledge base that feeds it.
Ensure the health of your knowledge base
In spite of its name, generative AI (AI that can generate text, code, or even images and music) can’t create something from nothing. If your knowledge base isn’t healthy—if the information it contains is incomplete, inaccurate, out-of-date, or devoid of context—your KBS won’t perform to a high level.
Garbage in, garbage out
The old computing adage about “garbage in, garbage out” applies to knowledge-based systems, too. Your KBS is dependent on the training data you provide, so if that data is low-quality, the AI can start “hallucinating”: suggesting incorrect, incomplete, or misleading answers that can cause headaches for your team. When this happens, employees’ trust in the knowledge platform starts to erode. When trust erodes, fewer people contribute to the growth and upkeep of your knowledge base, and its information grows even more stale. It’s a vicious cycle you can avoid by giving your KBS what it needs to support your decision-making.
Your knowledge base should be:
- Accurate and trustworthy, with answers vetted by knowledgeable users
- Easy to update and refresh as new information emerges
- Capable of capturing the context in which questions are asked and answered
- Continuously improving and self-sustaining
The role of knowledge management
It’s essential to have a knowledge management (KM) approach that enables discussion and collaboration, since this allows you to improve the quality of your knowledge base by evaluating the KBS’s responses and refining the structure of your queries to yield better answers. This pattern of interaction—humans applying their judgment to the quality and accuracy of the AI-generated output—will help you get even better results from your KBS over time.
Keep in mind that your framework for knowledge management should line up with how your employees already search for and find answers. Users should be able to capture knowledge, discover answers to their questions, and learn from domain experts without disrupting their existing workflows.
The future of knowledge-based systems
As AI continues to evolve, knowledge-based systems will become more capable, with the potential to entirely reshape how we work.
In a marketing context, KBSes won’t just analyze static data; they’ll unpack the nuances of customer behavior. They’ll pick up on the sentiments behind social media posts, interpret the tone of online reviews, and predict customer trends before they gain mainstream momentum.
In sales, these systems will evolve to understand complex sales dynamics and buyer psychology. They’ll analyze historical sales data, market trends, competitor strategies, and individual buying behaviors to create comprehensive sales strategies.
In customer service, KBSes will become sophisticated, empathetic virtual customer service agents who understand not only the customer’s issue, but the feelings and context behind it. They’ll be capable of perceiving frustration, confusion, or urgency and respond accordingly, just as a human would.
Quality in, quality out
Knowledge-based systems are a powerful AI tool. Thanks to their ability to leverage expert knowledge and their robust reasoning systems, KBSes can help humans make critical decisions, weigh costs and rewards, and get up to speed quickly on a new topic. But for your KBS to deliver on its full potential, it needs a high-quality knowledge base to draw from.