foreign to enable a computer system to understand human language it must have a way to represent knowledge and meaning in a form that system can work with this is where knowledge representation comes in knowledge representation involves designing a formal approach to represent knowledge in a way a computer can process in artificial intelligence knowledge representation is the process of presenting information about the real world in a way that a computer system can comprehend and use knowledge representation aims to give computers a method to reason about the real world make choices and Reserve issues based on the information that is available to them natural language processing relies heavily on the knowledge representation since it gives the text Data a way to represent and manipulate meaning NLP deals with understanding and processing human language which involves understanding the meaning of words and sentence in NLP knowledge representation represents meaning in text Data such as sentences paragraphs and documents this representation enables NLP systems to analyze and manipulate the meaning of Text data in various ways such as information retrieval question answering text summarization sentiment analysis and Mission translation overall knowledge representation is a crucial component of NLP because it gives text Data with ability to represent and manipulate meaning which is necessary for computers to understand and process human language of after all NLP is a subfield of artificial intelligence that deals with the interaction between computers and humans using natural language NLP involves in a wide range of tasks such as text classification Mission translation chatbots and many more if you want to become an expert in the field of AI and ml then take up the professional certificate program in Ai and ml by simplyler and master those necessary skills check out the link in the description for more details knowledge representation is a critical component of artificial intelligence and a rapidly growing field there are many exciting career opportunities available for professionals with expertise in this area some of the most popular career paths in knowledge representation and AI include AI engineer natural language processing specialist EI researcher knowledge engineer chatbot developer data scientist and many more the average base income of an AI engineer in India is rupees 9 lakh 80 000 per year whereas the average base pay of an AI engineer in the United States is dollar 1285 per annum it's worth noting that AI is a rapidly growing field in India as there is a high demand for skilled Professionals in this area which can result in higher salaries and attractive compensation package for qualified candidates on that note welcome to the simply learn YouTube channel today we are here with an interesting video on knowledge representation in EI without any further delay let's have a look at the agenda so first we'll start with what is knowledge representation next what kind of knowledge to represent in AI advancing we will understand the types of knowledge moving ahead let's have a look at the AI knowledge cycle and then the properties and finally the approaches of knowledge representation before going deep into the topic let us understand knowledge representation with an example so imagine you are organizing a party and you need to keep a track of your guest dietary restrictions so you could represent this information in a table that lists each guest and their dietary restriction so in this table each row represents a guest and the column represent their name and dietary restriction organizing this information in a structured way allows you to reference it as needed throughout the party planning process quickly so this is an example of knowledge representation because you are using a system to represent information meaningfully that can be easily accessed and used other example of knowledge representation include using a graph to represent relationships between concept or a decision tree to represent a decision making process now we got a basic idea of what is knowledge representation in EI so moving ahead let's have a look at the various kinds of knowledge that AI needs to represent so first object so an object is a thing or entity that can be identified and described so for example a car a person or a book are all objects coming to events and event happens at a specific time and place for example a wedding a concert or a game or all events next is performance performance is a measure of how well a task is accomplished for example in sports performance might be how many points a player scores or how fast a runner completes a race next we have meta knowledge So Meta knowledge refers to knowledge about knowledge it is the knowledge that describes how other pieces of knowledge are related to each other for example knowing that a car is a type of vehicle is an example of meta knowledge next we have facts so facts are statements that are true or false for example this guy is blue is a fact knowledge base a knowledge base is a collection of knowledge and information that is organized and stored in a specific way for example a customer information database is a type of knowledge base to provide an example of these Concepts consider the domain of a car dealership in this domain an object might be a specific car model such as the Toyota Camry and even may be a test driver or a purchase of a car so performance might be how well a salesperson can sell a specific car model and meta knowledge suggests that the Toyota Camry is a certain type a fact might be that the Cadbury has a particular fuel efficiency rating finally the knowledge base might include information about the customer preference sales data and vehicle specification so next let's discuss on the types of knowledges first we have declarative knowledge this is factual knowledge about the world including information about objects Concepts events and relationships declarative knowledge can be represented as a set of propositions or statements next we have procedural knowledge this is knowledge about how to do things including skills procedures and techniques so procedural knowledge can be represented as a set of rules or algorithms and next we have meta knowledge So Meta knowledge refers to the knowledge that describes or characterize other knowledge it provides information about other knowledge properties relationships and context next we have heuristic knowledge refers to knowledge occurring those trial and error and it often based on experienced rather than formal rules or logical reasoning and finally we have structural knowledge structural knowledge refers to the organization and arrangement of information or data meaningfully structural knowledge is used to create models that describes the relationships between different concepts or entities in knowledge representation so following to this we have cycle of knowledge representation in AI so perception so perception is the process by which information is gathered through the sense and processed by the brain in the context of knowledge representation perception refers to the ability of an AI system to sense and interact with the real world and extract meaningful information from it then we have learning so learning is gaining new knowledge skills or behavior through experience study or instruction in the context of knowledge representation learning refers to the ability of a system to occur new information and modify its internal knowledge representation based on that information and then we have knowledge representation and reasoning so knowledge representation is creating a model of knowledge in a computer system that can be used for reasoning and decision making the reasoning is used that model to conclude make inferences and solve problems so in the context of knowledge representation the goal is to represent knowledge in a way that it is efficient and effective for reasoning and then we have planning planning is creating a sequence of actions to achieve a goal in the context of knowledge representation planning refers to the ability of a system to create a plan of action based on its internal knowledge representation and finally we have execution execution is a process of carrying out a plan of action in the context of knowledge representation execution refers to the ability of the system to implement a plan of action based on its internal knowledge representation and the environment Factor it perceives now let's discuss the properties different properties of knowledge representation including expressiveness a knowledge representations system should be able to express a wide range of Concepts and relationships between them the next property is inferential adequacy a knowledge representation system should support the ability to reason with the represented knowledge the next property is efficiency a knowledge representation system should be able to manipulate and retrieve knowledge efficiently next transparency a knowledge representation system should be transferred to the user allowing them to understand and modify the knowledge quickly and finally scalability a knowledge representation system should be able to handle large amounts of data and still maintain its efficiency and expressiveness moving ahead let's have a look at the approaches of knowledge representation knowledge representation is an essential aspect of artificial intelligence that involves organizing and structuring knowledge in a way that computer systems can effectively utilize there are different approaches of knowledge representation in AI including simple relational inheritable inferential and procedural knowledge so let's explore each of these approaches of knowledge representation with an example first we have SIMPLE relational knowledge this type of knowledge representation involves organizing knowledge through relationships between entities or objects simple relational knowledge is typically a set of rules defining the relationships between different objects for example a simple relational knowledge representation for a family could be John is the father of Mary Mary is the sister of Peter Peter is the son of John next inheritable knowledge inheritable knowledge represents the knowledge that can be passed on from one object or entity to another so this type of knowledge representation is often used to represent hierarchical relationships between objects for example an animal that belongs to the class of mammals inherits all the attributes of its parent class so in this case the inheritable knowledge is represented as mammals are warm blooded animals dogs are mammals therefore dogs are also warm-bladed animals next inferential knowledge inferential knowledge represents knowledge derived from another knowledge so this type of knowledge representation is often used to represent logical relationships between objects for example in a medical diagnosis system a doctor might infer a patient's condition based on their symptoms so the inferential knowledge is represented as if a patient has a fever and a cough they might have pneumonia the patient has a fever and a cough therefore the patient might have pneumonia next procedural knowledge procedural knowledge represents the knowledge that involves a sequence of actions or steps to achieve a particular goal so this knowledge representation is often used in expert systems or intelligent agents performing tasks or solving problems for example a procedural knowledge representation for making a cup of tea could be boiled water in a kettle put a tea bag in a cup pour the hot water into the cup wait for a few minutes remove the tea bag and add sugar or milk as desired so overall knowledge representation is a critical aspect of AI and different types of knowledge representation helps to organize knowledge in ways that the computer system can effectively utilize so guys what are your thoughts on how effective knowledge representation is for AI system do let me know in the comment section if you like this video share it with your friends before I sign off make sure you subscribe to our Channel and hit the Bell icon to get all the updates we'll be back with more such interesting videos until then keep learning and stay tuned to Simply learn hi there if you like this video subscribe to the simply learned YouTube channel and click here to watch similar videos turn it up and get certified click here