Knowledge representation has always been one of the most critical components of artificial intelligence. Without any doubt, artificial intelligence relies on information as its primary source and this information acts as a raw material for knowledge representation. When we speak of knowledge representation as a concept in artificial intelligence, we are describing the various means through which knowledge can be channelized into the neural schema of machines. This would enable machines to function and perform better. The faster that we are able to achieve this task, the greater will be the progress that will be achieved in artificial intelligence. As such various types of data scientists who want to make AI systems smarter use knowledge as the primary raw material to do so. In this article, we take a look at knowledge representation and its importance for Artificial Intelligence in greater detail.

Knowing more about knowledge representation

Before we establish the importance of knowledge representation in AI, it is important to analyze the various sources from which information is gathered. Knowledge is primarily gathered in the form of objects and events. While objects represent direct sources of information, events are what we perceive as a continuity of various processes. This kind of knowledge that is known to us directly or indirectly from various sources is called meta-knowledge.


Knowledge can further be classified into the following types. When we give the description of any object, fact, or thing, it is called declarative knowledge. When we describe the working of a process in the form of certain procedural aspects, it is called procedural knowledge. Procedural knowledge describes the working and behavior of various things and processes. This type of knowledge helps in framing rules and devising strategies. We may also use the superimposition principle to club various types of knowledge and the type of knowledge derived after superposition is called meta-knowledge. Similarly, when the knowledge is gathered from the experience of professionals, it is called heuristic knowledge. This type of knowledge is extremely important and critical for solving problems and other aspects of decision-making. Finally, the last type of knowledge that we talk of is called structural knowledge. Structural knowledge is used to establish various linkages between concepts and ideas so that we can use it for deriving solutions to some of the most complex problems.

Lifecycle of AI and linkages with knowledge representation

It is important to understand the entire life cycle of Artificial Intelligence and the contribution of knowledge representation in this life cycle. For any AI system to function effectively, it is important that it perceives information from its surroundings. This can be done with the help of various kinds of sensors and other objects. When an AI system gains familiarity with its environment, it becomes fully equipped to interact with it. To perceive information from the environment, structured inputs are required in the form of audio, video, text, etc. Knowledge representation plays a key role in this. After this, we come to the process of deep learning. Different types of algorithms are used to train the system powered by artificial intelligence. It is the reasoning block that enables an AI system to take effective decisions. Needless to mention, knowledge representation is a critical component of the reasoning block. Finally, we come to the execution of various decisions. Input is taken from the knowledge unit as well as the reasoning unit to execute the planned actions with a lot of precision.


There are different types of techniques that are used to represent knowledge for their proper usage in AI systems. The most important among these is logical representation. With the help of well-defined rules, any type of ambiguity is avoided and logic is established. We may use the boolean method to establish logic or take the help of the prepositional method. Other techniques that we use include semantic rules, production rules, and representation in the form of frames.

Concluding remarks

The domain of knowledge representation is widening the base and applications of artificial intelligence. In the future, we are likely to see new and advanced systems being developed with help of a perfect juxtaposition between artificial Intelligence and knowledge discovery.