To enable a computer system to understand human language it must have a way to represent knowledge and meaning in a form that the 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. Here we are going to discuss knowledge representation using the semantic network.
What is Knowledge Representation
Overall knowledge representation is a crucial component of NLP because it gives text Data the ability to represent and manipulate meaning. Which is necessary for computers to understand and process human language. After all, NLP is a subfield of artificial intelligence. It deals with the interaction between computers and humans using natural language.
NLP involves a wide range of tasks such as text classification, Mission translation chatbots, and many more.
Knowledge Representation in AI
In AI 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 Resolve issues based on the information that is available to them.
Natural language processing relies heavily on 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 sentences 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
- Mission translation
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
- AI Researcher
- Knowledge Engineer
- Chatbot Developer
- Data Scientist
Before going deep into the topic let us understand knowledge representation with an example:
Imagine you are organizing a party and you need to keep track of your guests’ dietary restrictions. 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 represents 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 examples of knowledge representation include using a graph to represent relationships between concepts or a decision tree to represent a decision-making process.
Now, we have a basic idea of what knowledge representation is in AI so moving ahead let’s have a look at the various kinds of knowledge that AI needs to represent.
Kinds of Knowledge that AI Needs to Represent
The first is an object so an object is a thing or entity that can be identified and described for example a car, a person, or a book are all objects.
Events happen at a specific time and place for example a wedding, a concert a game, or all events.
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.
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.
Facts are statements that are true or false for example this guy is blue is a fact.
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 provide an example of these Concepts and 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 maybe 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.
Type of 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.
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.
Meta knowledge refers to the knowledge that describes or characterizes other knowledge; it provides information about other knowledge properties, relationships, and contexts.
It refers to knowledge occurring through trial and error and it is often based on experience rather than formal rules or logical reasoning.
Structural knowledge refers to the organization and arrangement of information or data meaningfully structural knowledge is used to create models that describe the relationships between different concepts or entities.
AI Knowledge Cycle
The Cycle of Knowledge Representation in AI
So, perception is the process by which information is gathered through the senses 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.
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 acquire new information and modify its internal knowledge representation based on that information.
Knowledge Representation and Reasoning in AI
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 in 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 is efficient and effective for reasoning.
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.
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.
Properties of Knowledge Representation
A knowledge representation system should be able to express a wide range of Concepts and relationships between them.
A knowledge representation system should support the ability to reason with the represented knowledge.
A knowledge representation system should be able to manipulate and
retrieve knowledge efficiently.
A knowledge representation system should be transferred to the user allowing them to understand and modify the knowledge quickly.
A knowledge representation system should be able to handle large amounts of data and still maintain its efficiency and expressiveness.
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 to knowledge representation in AI including:
- Simple Relational
- Procedural knowledge
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.
The 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-blooded animals.
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.
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, putting a tea bag in a cup, pouring the hot water into the cup, waiting for a few minutes, removing 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 help to organize knowledge in ways that the computer system can effectively utilize.