Knowledge Representation in AI (Semantic Networks)

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Knowledge representation

What is Knowledge Representation?

Knowledge Representation and Reasoning (KR, KRR) represents information from the real world for a computer to understand and then utilize it to solve complex real-life problems like communicating with human beings in natural language.

Different Types of Knowledge Represented in AI

  • Objects
  • Events
  • Performance
  • Facts
  • Meta-Knowledge
  • Knowledge-Base

Types of Knowledge in AI

There are five types of knowledge such as:

Declarative Knowledge: This includes concepts, facts, and objects expressed in a declarative sentence.

Structural Knowledge: It is basic problem-solving knowledge that describes the basic concepts between concepts and objects.

Procedural Knowledge: This is responsible for knowing how to do something, including rules, strategies, and procedures, etc.

Meta Knowledge: This defines the knowledge about other types of knowledge.

Heuristic Knowledge: This represents some expert knowledge in the field or subject.

The cycle of Knowledge Representation

Artificial intelligence systems usually consist of various components to display their intelligent behaviour. These components are as follows:

Here is an example to explain the different components of the system and how it works. This diagram shows the interaction of the artificial intelligence system with the real world and the components involved in showing the intelligence.

Knowledge representation

Perception: The perception component retrieves data or information from the environment. With the help of this component, the user can retrieve data from different environments, find out the source of noises and determine if the AI is damaged by anything. Also, it defines how to respond to any sync that has been detected.

Learning: This learns from the captured data by the perception component. Here the goal is to build computers that can be taught instead of programming. Learning focuses on the process of self-improvement in order to learn and understand new things; the system requirements are knowledge acquisition, inference acquisition of heuristics, fastest searches, etc.

Knowledge Representation and Reasoning: This shows the human-like intelligence in the machine. Knowledge representation is all about understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behaviour from the top down and focus on what an agent needs to know in order to behave intelligently. It also defines how automated reasoning procedures can make this knowledge available as needed.

Planning and Execution: These components depend on knowledge representation analysis and reasoning. Here planning includes giving an initial state, finding the pre-condition, effects, and a sequence of actions to achieve a state in which a particular goal holds. Once the planning is complete, the final stage is the execution of the entire process.

Relationship Between Knowledge & Intelligence

In the real world, knowledge plays an important role in intelligence as well as creating artificial intelligence. It demonstrates intelligent behaviour in AI agents or systems. Now it is possible for an agent or system to be accurate on some input only when it has the knowledge or experience about the input.


  • Logic Representation
  • Semantic Network Representation
  • Frame Representation
  • Production Rules

Logic Representation: It’s a language with some definite rules which deal with propositions and has no ambiguity in representation. It proposes a conclusion based on various conditions and lays down some important communication rules.


  • Syntax decides how we can construct legal sentences in logic.
  • It determines which symbol the user can use in knowledge representation.
  • Also, how to write those symbols.


  • Semantic is the rule by which we can interpret the sentence in logic.


  • Logical representation allows performing logical reasoning.
  • This representation is the basics of programming languages.


  • Logical representation has some restrictions and is challenging to work with.
  • This technique may not be very natural, and inference may not be very efficient.

Semantic Network Representation

Semantic networks work as an alternative to predicate logic for knowledge representation. In semantic networks, the user can represent their knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs which describe the relationship between those objects. This representation consists of two types of relations, such as IS-A relationship (Inheritance) and Kind-Of-Relation.


  • Semantic networks are a natural representation of knowledge.
  • It transparently conveys meaning.
  • These networks are simple and easy to understand.


  • Semantic networks take more computational time at runtime.
  • These are inadequate as they do not have any equivalent quantifiers.
  • These networks are not intelligent and depend on the creator of the system.

Frame Representation

A frame is a record-like structure that consists of a collection of attributes and values to describe an entity in the world. These are the AI data structures that divide knowledge into substructures by representing stereotypical situations. It’s a collection of slots and slot values of different types and sizes. Slots have been identified by names and values, which are called facets.


  • It makes the programming easier by grouping the related data.
  • Frame representation is easy to understand and visualize.
  • It is very easy to add slots for new attributes and relations.
  • Also, it is easy to include default data and search for missing values.


  • In frame system inference, the mechanism cannot be easily processed.
  • The inference mechanism cannot be smoothly proceeded by frame representation.

Production Rules

In production rules, the agent checks for the condition, and if the condition exists, then the production rule fires, and corresponding action is carried out.


  • The production rules are expressed in natural language.
  • The production rules are modular and can be simply removed or modified.


  • It does not exhibit any learning capabilities and does not store the result of the problem for future uses.
  • During the execution of the program, many rules may be active. Thus, rule-based production systems are inefficient.

Representation Requirements

  • Representational Accuracy
  • Inferential Adequacy
  • Inferential Efficiency
  • Acquisition Efficiency

Author: SVCIT Editorial

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