As AI keeps developing at an astonishing pace, a major problem keeps nagging those working at the forefront of the AI revolution. You guessed it right, how to make AI more human-like. How to make it think like us and naturally deduce logical conclusions. Knowledge representation allows us to create exceptionally intelligent AI systems. These systems are capable of using stored data, understanding the relationships between the data points, and drawing intelligent and unique conclusions from them. In this article, we will go further into knowledge representation in AI, elaborate on some of its common uses, and give some advice for students and professionals looking to get upskilled in AI.

What is Knowledge Representation in AI?

Knowledge representation in AI refers to the storage of real-world data in the form of symbols. These symbols can then be used by algorithms for deducing meaningful and logical conclusions. But why? The logic behind representing knowledge in such a format is very simple. Traditional data storage methods are just not good enough for solving increasingly complex problems.

Ontology Engineering

As the field of AI expands to new domains, and its capabilities scale up, demands for more and more data from varying fields will keep on increasing exponentially. And more importantly, these knowledge representation modules must be compatible with each other. This is where the discipline of ontology engineering comes in.  The main focus is on designing and creating large, cross-compatible knowledge bases.

Real-World Examples of Knowledge Representation

Knowledge Representation in Language Processing Tasks

In language processing tasks, knowledge representation plays an integral part. As we already know, knowledge representation is essentially the process of storing semantic information and deriving logical conclusions from it. Ontology is the backbone of such conclusions. Quite naturally, in natural language processing, knowledge representation is used for storing information and using that information to create text analysis models or chatbots.

Knowledge Representation in Expert Systems

Knowledge representation helps in creating advanced expert systems. These systems are capable of replicating expertise in a specific field. Most might be unaware of the fact that expert systems have seen their demand rise exponentially over the years.

  1. Medical field:One of the most significant uses of expert systems is in the medical field. Expert systems are used for detecting diseases, predicting vulnerability to chronic diseases, predicting outbreaks, and many other things. Knowledge representation enables them to match various symptoms with existing databases to accurately predict the disease profile of a patient. As these systems and databases improve, medical expert systems could even be used effectively in the drug discovery pipeline. Accelerating the growth of modern medical science and taking it to unforeseen heights.
  2. Grammar Checkers: Another common use of expert systems can be seen in grammar-checking services like Grammarly or Quilbot, where they can provide suggestions when checking for grammatical mistakes in a text. Now one might think that the same service has been available in Microsoft Word for decades. Well, if you are thinking like that, you would be surprised to know that these services go much deeper. They can provide suggestions based on various types of writing styles, such as making sentences flow more naturally, improving readability, and so on.

Tips for Professionals and Students Looking to Upskill in AI

As these expert systems and language processing modules become smarter and smarter, it will create significant strain on the labor force. For the time being, these systems are nowhere near replacing humans, yet upskilling in AI and domains such as knowledge representation in AI helps individuals find better job prospects. Therefore, professionals and students should give serious thought to AI upskilling courses.

When choosing an upskilling course in AI, individuals will be presented with a platform of options where course variations reach mind-boggling numbers. In such a situation, how could an individual find a course that is worthy of investment?

Shortlist Reputable Institutions

When choosing a course, the very first consideration should be identifying the institution. Look through the courses that are being offered by reputable and accredited institutions. Make a shortlist of institutions and then proceed further.

Identify Individual Needs

Identifying individual goals and needs is crucial to finding the best courses. For example, a beginner should look at entry-level courses that will help them familiarise themselves with rudimentary concepts, whereas a more technically advanced individual will look at courses that cover more advanced topics such as knowledge representation in AI, and so on. Choose courses that align with your capabilities and aspirations.


Knowledge representation in AI has proven pivotal in the exponential progress of AI technologies. From diagnosing diseases to the autocorrect feature on your smartphone, we can see the manifold advantages of these advanced systems. Individuals who are willing to partake in this AI revolution must prepare themselves by gaining relevant education in the field, hence opening up vast opportunities for themselves and the world.