Knowledge Representation in AI and Its Business Significance

Artificial Intelligence is the most innovative technology that has always captivated human beings. Robots that can think, act, decode complex information, and make smart decisions continue to inspire tech enthusiasts, sci-fi novels, and movie makers.
Humans know things, which we term knowledge, and all our abilities to perform various actions in the real world originates from the knowledge we have gathered so far.
If we were to make Artificial Intelligence programs more sophisticated and capable of imitating human intelligence in a given scenario, we would be required to feed the AI systems with more and often complex information about the real-world. Now that leads us to the concept of Knowledge Representation in Artificial Intelligence.

What is Knowledge Representation?

Knowledge representation is a field of artificial intelligence that allows AI programs to answer questions intelligently and make deductions about real-world facts. It refers to representing information about the world in a way that a computer system can understand and use it to solve real-life problems or handle real-life tasks.
Knowledge representation in AI concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs. For instance, an AI software can be trained to solve complex tasks such as diagnosing a medical condition only if the relevant knowledge is made available to the AI system as required.

There are two primary concepts in Knowledge Representation:

#1Knowledge

Knowledge refers to the fact of knowing something that has been gained through an experience and learning which makes the agent (AI application like a chatbot) familiar.
In Artificial Intelligence, a machine will perform a specific action based on a specific condition only if it has gained an experience from the past. For example, an AI agent can solve a chessboard puzzle only if it has gained sufficient knowledge on how to solve the puzzle and win the game.

#2Representation

Representation refers to the process of representing what has been gained from the knowledge. Representation consists of the objectives that are used to express the knowledge that is required to solve a specific problem.
The different kinds of knowledge that need to be represented in AI systems include:

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Different Types of Knowledge Represented in AI

In a broad sense, knowledge is the awareness or familiarity gained by the experiences of facts, data, and situations. Following are the different types of knowledge that need to be represented in artificial intelligence.

Declarative Knowledge

Refers to the facts, objects, and concepts that allow us to describe the world around us. It shares the description of something expressed in declarative sentences which is simpler than procedural language.

Structural Knowledge

Constitutes the problem-solving knowledge that describes the relationship between various concepts or objects and their descriptions.

Procedural Knowledge

Also known as imperative knowledge, procedural knowledge is used to complete any task with specific rules, strategies, processes, or agendas. It’s the type of knowledge which is responsible for knowing how to do a particular task and hence relies on the task we are trying to finish.

Meta Knowledge

As mentioned above, meta knowledge refers to predefined knowledge about things that we are already aware of. This knowledge typically includes the study of tagging, planning, learning, etc.

Heuristic Knowledge

Also known as shallow knowledge, heuristic knowledge is highly used in the process of reasoning as it can solve issues based on the experiences of past problems. Thus, it provides a knowledge-based approach to define a problem and take action.

Four Fundamental Knowledge Representation Techniques in AI

In artificial intelligence, knowledge can be represented in different ways depending on the structure of the knowledge, perspective of the designer or even based on the type of internal structure used. An effective knowledge representation should be rich enough to include the knowledge required to solve the problem. It should be natural, compact and maintainable.
Here are the four fundamental knowledge representation techniques used in AI:
1. Logical Representation
Knowledge and logical reasoning play an integral role in artificial intelligence. However, you often require more than just general and powerful methods to ensure intelligent behavior. Formal logic is the most helpful tool in this area. It is a language with unambiguous representation guided by certain concrete rules.
Knowledge representation relies heavily not so much on what logic is used but on the method of logic used to understand or decode knowledge.
Logical representation technique allows designers to lay down certain vital communication rules to share and acquire information to and from agents with minimum errors in communication. Different rules of logic allow you to represent different things resulting in an efficient inference. Hence, the knowledge acquired by logical AI agents will be definite which means it will either be true or false.
Although working with logical representation is challenging, it forms the basis of most of the programming languages used currently and enables you to construct logical reasoning.
2. Semantic Network
A semantic network allows you to store knowledge in the form of a graphic network with nodes and arcs representing objects and their relationships. It could represent physical objects or concepts or even situations. A semantic network is generally used to represent data or reveal structure. It is also used to support conceptual editing and navigation.
A semantic network is simple and easy to implement and understand. It is more natural than logical representation. Semantic network allows you to categorize objects in various forms and then link those objects. It also has greater expressiveness than logic representation.
3. Frame Representation
A frame is a collection of attributes and its associated values, which describes an entity in the real world. It is a record-like structure consisting of slots and their values. Slots could be of varying sizes and types. These slots have names and values. Or they could have subfields named as facets that allow you to put constraints on the frames.
There is no restraint or limit on the value of facets a slot could have, or the number of facets a slot could have or the number of slots a frame could have. Since a single frame is not very useful, building a frame system by collecting frames that are connected to each other will be more beneficial. It’s a flexible knowledge representation technique used by various AI applications.
4. Production Rules
Production rule-based representation has many properties essential for knowledge representation. It consists of production rules, working memory, and recognize-act-cycle. It is also called condition-action rules. If the condition of a rule is true according to the current database, the action associated with the rule is performed.
Although production rules lack precise semantics for the rules and are not always efficient, the rules lead to a higher degree of modularity. And it is the most expressive knowledge representation system.

Cycle of Knowledge Representation in AI

When building an AI system, it’s important to come up with a knowledge representation system that will help in feeding the AI system with the essential knowledge. The AI knowledge cycle consists of some major components to demonstrate intelligent behavior that make knowledge representation possible.

Approaches to Knowledge Representation in AI

There are four main approaches to knowledge representation in artificial intelligence. Each approach corresponds to a technique used to represent the knowledge discussed above.
1. Simple Relational Knowledge
2. Inheritable Knowledge
3. Inferential Knowledge
4. Procedural Knowledge
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Properties of a Good Knowledge Representation System

A good knowledge representation system should meet the following requirements:

Representational adequacy

The knowledge representation (KR) system should be capable of representing each kind of required knowledge in a way the AI system can understand.

Inferential adequacy

The KR system should be flexible enough to manipulate existing knowledge to make way for new knowledge corresponding to the present structure.

Inferential efficiency

Inferential efficiency refers to the ability of the KR system to direct the inferential knowledge mechanism toward the most productive directions using appropriate guides.

Acquisitional efficiency

Acquisitional efficiency is the ability of the knowledge representation system to automatically acquire new knowledge, integrate the new information into the existing knowledge base, and use the same to improve efficiency and productivity.

Why is Knowledge Representation Important for AI Systems?

Knowledge representation equips AI agents with the capabilities to solve the most complex tasks based on what they have learned from the knowledge given to them. The knowledge given to them could be human experiences, problem-solutions, if-then rules, response to specific scenarios, etc. which are represented in a specific way for the AI agent to understand and learn.
Knowledge representation is the technique that runs behind several technologies and machines that are around us. An AI software development can solve complex problems or complete difficult tasks successfully only by relying on a knowledge base which describes how to approach and perform each task.
Knowledge representation is a field of AI concerned with understanding, designing, and implementing ways of representing information in a comprehensible manner for the machines, so the AI programs can use this information to:
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Benefits of Knowledge Representation in AI

Knowledge representation is the driving force that equips your AI program to support you in improving productivity, increasing competitive advantage, and minimizing risks and errors. Knowledge representation in AI delivers several benefits.
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How Can Fingent Help

Knowledge representation is the key to designing AI agents that can think and act smart while ensuring that such thinking can constructively contribute to their behavior and allow them to respond effectively to each scenario. However, it is important to choose the right type of knowledge representation if you want to ensure business success with AI.
There are certain considerations to keep in mind when designing a knowledge representation system. Factors such as the structure for storing knowledge, depth of information required for representing a subject adequately, etc. are some of the common dilemmas faced during the creation of knowledge systems.
At Fingent top custom software development company, we help you build custom AI applications that are well-tested and configured to process specialized data sets in order to produce expected results. Our AI experts can collaborate with you and help provide different types of knowledge to your AI systems to make your AI applications more competent and sophisticated.
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Frequently Asked Questions

Knowledge representation is a subfield of artificial intelligence that solely focuses on representing information about the real world around us in a way that computers can understand. For instance, knowledge representation takes all the concepts in a domain, describes how these concepts link to each other, and defines the rules that govern the behavior of the AI system based on each condition.
Following are the different types of knowledge representation used in artificial intelligence.
  • >> Declarative Knowledge: Constitutes the facts, objects, and concepts that allow us to describe the world around us.
  • >> Structural Knowledge: Includes the problem-solving knowledge that describes the relationship between various concepts or objects and their descriptions.
  • >> Procedural Knowledge: Used to complete any task with specific rules, strategies, processes, or agendas.
  • >> Meta Knowledge: Constitutes the predefined knowledge about things that we are already aware of.
  • >> Heuristic Knowledge: Used in the process of reasoning as it can solve issues based on the experiences of past problems.
AI agents can express intelligent behavior only if they acquire the knowledge and experiences that we, humans, have gained from the real world around us. Knowledge representation allows AI programs to use the information they’ve learned to:
  • >> Derive information that is implied by the AI agent,
  • >> Communicate with people in natural language,
  • >> Decide what to do next,
  • >> Plan future activities, and
  • >> Solve problems in areas that normally require human expertise.
It’s important to choose a knowledge representation system with the following characteristics:
  • >> KR system should be extensive, well-represented, and easily decipherable
  • >> Should cover a wide range of standard computing procedures to support large scale application
  • >> Must be easy to access and provide the options to identify events and decode the reaction of different components
The AI knowledge cycle comprises multiple elements or entities that are used to represent and utilize knowledge. These entities include perception, learning, knowledge, reasoning, planning, and execution.
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