Knowledge Representation In Ai

Knowledge Representation in AI

Knowledge Representation in AI – Importance Techniques to Know

Knowledge Representation in Artificial Intelligence is making machines identify things with the knowledge like a human in understanding, interpreting, and analyzing. We aim to provide the reader with how knowledge representation is functioning in AI for the machines to utilize through knowledge representation in this AI blog. We cover types, properties, and popular approaches to knowledge representation and, surely, you will have a better understanding of knowledge representation in AI after reading this article.

What is Knowledge Representation?

We, humans, are good at reasoning, understanding, analyzing, and interpreting what we are seeing in our daily life. We know how to react and respond to the situation we are going through. But how to make machines perform similar actions? Here comes the knowledge representation in AI that equips the machines to perform understanding and interpreting the queries of the real world. Knowledge Representation in AI is the study of how to equip intelligent machines with beliefs, intentions, and judgments to express according to the situations through automated reasoning.

Knowledge Representation familiarly known as KR or KRR (Knowledge Representation and Reasoning) is the representation of the information that is extracted from the real world and utilized for solving complex problems. One of the examples is communicating with human beings through Natural Language for answering their queries that of. It is not about simply storing the data in a database but allows machines to learn from real-world information and behave smart like a human.

What knowledge to represent?

There are various kinds of knowledge needed to be represented in AI like objects, events, performance, facts, meta-knowledge, and knowledge-base.

  • Objects are the real-world things such as guitar, pencil, or dress
  • Events are the actions that are occurring in the real-world
  • Performance is the behavior description that involves knowledge to perform particular things
  • Meta-knowledge is the information about what should be known
  • Facts are the factors of real-world and what to represent
  • Knowledge-base is the central component of the agents that are knowledge-centric. It might be sentences, technical terms, or keywords in the English Language.

What are the types of knowledge representation?

We must identify and classify the various types of knowledge to represent knowledge to machines and it is a very important thing of knowledge representation in Artificial Intelligence. Following are the popular and important terms with definitions to understand which knowledge can present in AI.

  • Declarative Knowledge represents objects, facts, concepts, and so on that help us define the real world around us.
  • Procedural Knowledge refers to a bit more complex idea than declarative knowledge. Some of the examples are how things behave and work, how to react to the situation, etc. This knowledge helps machines to perform any task using particular procedures, strategies, and rules efficiently. Procedural knowledge depends on the process we are trying to perform.
  • Meta Knowledge is the group of information regarding other types of knowledge. Example: sentences
  • Heuristic Knowledge is provided by experts of various domains, disciplines, fields, and subjects that are obtained by experience to approach real-world problems for making better decisions.
  • Structural Knowledge is helping machines to establish a relationship between objects and concepts along with their descriptions to solve various kinds of real-world problems.

Knowledge Cycle of AI

An AI contains the following major components to showcase intelligent behavior that makes knowledge representation possible.

  • Perception
  • Learning
  • Knowledge Representation and Reasoning
  • Planning
  • Execution

Perception: This block helps the AI system obtain information about the surroundings using various kinds of sensors and makes the AI system familiar with the environment to interact efficiently according to it. AI can able to sense typical structure data or other forms of data like video, text, audio, time, temperature, or any sensor-centric input.

Learning: This block is used to help the AI system run the deep learning algorithms that are written here for making the AI system transfer the required information from the perception block to the learning block for learning and understanding.

Knowledge Representation and Reasoning: We use knowledge based on reasons to take any decision. These blocks are responsible for serving like humans through all the knowledge data and discovering the relevant ones to be offered to the learning model whenever it is needed.

Planning and Execution Blocks: These blocks are independent blocks that can work in tandem. They used to take the information from the knowledge block and the reasoning block. Based on it, they execute relevant actions.

Thus, knowledge representation is useful for AI machines to work smartly.

Important Properties of Knowledge Representation for AI Systems

It is important to create a knowledge representation system to represent the above-mentioned types of knowledge to be presented to machines. Following are the primary properties that help us assess the intelligent systems for knowledge representation.

Representational Adequacy: It is the major property that makes AI machines adequate for the understanding of all information needed to deal with a particular problem.

Inferential Adequacy: It is making AI machines with the flexibility enough to deal with the present knowledge for newly possessed knowledge.

Inferential Efficiency: It is about flawlessly adding knowledge efficiency as the representation system can’t able to accommodate new knowledge when the old knowledge is presented.

Acquisitional Efficiency: It is the ability to gain new knowledge automatically by helping AI machines to add current knowledge and increasing smarter and productiveness consequently.

Techniques of Knowledge Representation in AI

Following are the four major techniques of knowledge representation in artificial intelligence

Logical Representation: It is the primary form of knowledge representation to AI machines with a well-defined syntax and semantics that are used. It requires to have no definition of the meaning for dealing with prepositions. It acts as a communication rule and it can be used when representing facts to a machine it will be in two types, they are propositional logic and first-order logic. Propositional logic consists of statement logic or propositional calculus and it works in a Boolean that is a true or false method while first-order logic is also known as First Order Predicate Calculus Logic (FOPL) to represent objects in quantifiers and predicates with the advanced version of propositional logic.

Logical Representation
Logical Representation

Here, syntax and semantics will be used as we see earlier. The syntax is used to decide how can we construct legal sentences in logic and it determines which symbol can be used in knowledge representation. It also directs us on how to write symbols. Semantics, on the other hand, are the rules to tell us by which we can interpret the sentence in the logic and it assigns meaning to the sentences.

Pros of Logical Representation Technique

  • It helps in performing logical reasoning in knowledge representation
  • It is the fundamental for programming languages

Limitations of Logical Representation Technique

  • It has some restrictions and it is very challenging to work on this.
  • It may not be natural and inferences will not be efficient enough for real-world use cases.

Semantic Network Representation

Semantic Network is a graphical representation that is used to convey how the objects are connected and used with a data network. It consists of nodes/blocks as objects and arcs/edges as the connections for explaining how the objects are connected. This is an alternative for FOPL (First-Order Predicate Calculus Logic. It can be used in two types IS-A (Inheritance) and Instance (Kind-of-relation).

Pros of Semantic Network Representation

  • Natural representation
  • conveys meaning transparently
  • Easy and simple to understand
Limitations of Semantic Network Representation
  • More computational time required
  • Inadequate and no equivalent quantifiers were presented
  • Not intelligent and depend on the creator of the AI machine
Frame Representation

It is a record-like structure that contains a collection of attributes and values to define an entity in the real world. They are the AI structure that separates knowledge into substructures by representing stereotypes of situations. It consists of slot values of any size and type where slot names and values are known as facets.

Pros of Frame Representation
  • Programming will be very easier as grouping the relevant data
  • Easy to understand and visualize
  • Easy to include slots for new relations
  • Easy to add default data and look for missing values
Limitations of Frame Representations
  • This technique can’t be easily processed
  • Inference mechanism will not run smoothly
  • A very generalized technique
Production Rules

In this technique, the agent will check for the particular condition and if the condition exists, then the production rule action will be carried out. The condition part will define which rule might be implemented for a particular problem and the action part will carry out the associated problem-solving procedures. Therewith, the complete cycle is known as a recognized-act cycle. The production rule contains three major parts and they are the set of production rules, working memory, and the recognize-act-cycle.

Pros of Production Rules
  • Can be expressed in natural language
  • Highly modular and easy to update and remove
Limitations of Production Rules
  • It doesn’t show any learning capacities
  • It doesn’t save the result of the problem for future use
  • Many rules to be activated during the program execution
  • Inefficient rule-based production system
Approaches of Knowledge Representation in AI

The approaches are used to know how we can store the information in the AI system for knowledge representation. There are four different approaches to be followed in AI and they are simple relational knowledge, inheritable knowledge, inferential knowledge, and procedural knowledge.

Simple Relational Knowledge: It is the method to store facts and objects that are provided in columns and this approach is familiar in DBMS (Database Management System).

Inheritable Knowledge: This is based on a well-structured hierarchy of classes that are formed where data is saved that offers opportunities for inference. When we apply inheritance properly, it allows us to have inheritable information.

Inferential Knowledge: Here, logic will be used and it is a very formal approach as the facts can be extracted with a high level of accuracy.

Procedural Knowledge: In this approach, programs and codes will be used along with if-then rules. Programming languages like LIST and Prolog are storing information in this manner. However, we can’t use this approach for all forms of knowledge but domain-specific information can be very efficient for storing information in this way.


Knowledge Representation in AI is used to make the machines respond to the situation accordingly. Learn how it can be applied to real-world problems by enrolling in our Best AI Training Institute in Chennai. We have curated the AI Course with industry experts to provide the best-in-class coaching with hands-on exposure and course completion certification.

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