How would you describe all the knowledge in the world, and what would you do with it? That, in a nutshell, is the concern of knowledge representation (KR), a subfield of study within artificial intelligence (AI). It’s a process that takes all the concepts in a domain, establishes how these concepts relate to each other, and defines the rules that control how they behave.
To illustrate, think about the real world. It’s large and infinitely complex. So we can reduce it to an abstract model—for instance, a map that captures only aspects of the world that are relevant to us, such as its geography. But the computer can’t understand a map, so we reduce this even further into a set of rules and statements that represents the map. In other words, KR represents information in a way that computers can understand.
Read More about “Knowledge Representation”
KR is a necessary component of AI. It allows programmers to convert real-world data into a language that a computer can use to develop an information system (IS) that is critical in accomplishing tasks.
An example would be when a company needs to write a contract. It can use KR to state the intentions of the parties involved to ensure that the agreement reflects the best interests of all stakeholders. How can knowledge engineers achieve this? They do so by creating a sound KR system.
Properties a Good Knowledge Representation System Should Have
To be considered good, a KR system must have the following features:
1. Representational adequacy
It should be able to represent the different kinds of knowledge required.
2. Inferential adequacy
The KR system should be able to come up with new structures or knowledge that it can infer from the original or existing structures.
3. Inferential efficiency
It should be able to integrate additional mechanisms to existing knowledge structures to direct them toward promising directions.
4. Acquisitional efficiency
The KR system should be able to gain new knowledge through automated methods instead of relying on human intervention. However, it should also allow for the injection of information by a knowledge engineer.
To date, no single KR system has all of these properties.
Factors to Consider When Choosing a Knowledge Representation System
While finding the perfect KR system is not feasible for now, an effective one should have these characteristics:
As much as possible, KR systems must be extensive. All aspects that a KR system claims to consider must be well-represented and easily decipherable.
KR systems must cover a wide range of standard computing procedures to ensure widespread application.
KR systems must be easily accessible. Each domain or system must have the means to identify events and decipher how different components react.
KR systems must not contain unnecessary details that may only complicate processes.
All stakeholders must get an overview of processes and a clear understanding of the events that surround a KR system’s implementation.
All KR system outputs must be timely and accurate.
- KR, a subfield of AI, is a process that takes all the concepts in a domain, establishes how these concepts relate to each other, and defines the rules that control how they behave.
- An effective KR system has representational adequacy, inferential adequacy, inferential efficiency, and acquisitional efficiency.
- A good KR system candidate is comprehensive, computable, accessible, relevant, transparent, and concise.