Knowledge representation is one of the key aspects that help machines think and act like humans.
Further, it also encompasses the several ways we can represent knowledge in artificial intelligence. It represents knowledge in such a way that computers can understand and use this information to offer solutions for complex issues in the real world.
As a result, rather than just gathering and storing the information in a database, it also helps machines to learn the knowledge to resonate with human behaviour. In this article, we will discuss knowledge representation and its implications on businesses.
Knowledge Representation in AI
In artificial intelligence systems, knowledge is represented in the following manner:
Object
Events
Performance
Facts
Meta knowledge
Knowledge-base
An object represents the facts about objects, whereas events are actions that happen. Performance is the knowledge about implementing things. Facts are truths, and meta-knowledge represents the knowledge we know. A knowledge base is the foremost component of the knowledge base agent.
What are the Requirements for Knowledge Representation in Artificial Intelligence?
Many AI development services companies use knowledge graphs for AI-powered knowledge management. Knowledge representation should cover the following requirements:
- Representational Accuracy: It should be able to represent all types of required knowledge precisely.
- Inferential Adequacy: It should be able to handle the representational structure for arriving at the new structures, which should correspond to the new knowledge developed from the information given. It is otherwise called inferential adequacy
- Inferential Efficiency: The knowledge representation system should be capable of adding additional information to the knowledge structure so that it can be used to derive better inferences.
- Acquisitional Efficiency: It should be able to acquire new knowledge automatically without human assistance.
Constituents of Knowledge Representation System
A knowledge representation system should contain the following elements.
Perception
Learning
Knowledge representation and reasoning
Planning and Execution
Perception: involves extracting the knowledge and dissemination of the status of an AI system. It identifies any anomalies in the AI system and helps us decide if an AI system is working perfectly.
Learning: Captures the information that is sensed by the Perception element. It helps the system to learn the way humans do instead of relying on programming. Further, it emphasizes the improvement of the AI system.
Knowledge Representation and Reasoning: are used for acquiring knowledge in the most intelligent manner that ensures the AI agent behaves exactly like humans.
Planning and Execution: Provides optimized solutions to achieve an objective successfully. Execution represents the final stage, where the process gets implemented flawlessly.
Techniques of Knowledge Representation in AI
Logical Representation
Deals with certain conditions and gives key communication rules. It has syntax and semantics that support inferences. In logical representation, every sentence can be translated with the help of syntax. Logical representation executes logical reasoning, however, they have some limitations that the inference may not be precise.
Semantic Networks
Helps represent knowledge in graphical network form. It consists of nodes that represent objects and arcs that explain the connection between objects. It also categorizes and links the objects. Semantic networks help convey meaning in an efficient manner. But they take more computational time.
Frame Representation
A frame comprises several attributes and values. It splits the AI data structure into substructures. It facilitates programming by grouping related data while it is easy to understand. Also, we can add default data to find missing values. However, processing is a bit difficult.
Production Rules
In this technique, AI agents check for the condition, and if it exists, the needed action is implemented. The condition part decides the rule, and the action part involves solutions. The benefit is that the rules are expressed in natural language and can be changed or removed. Nevertheless, it cannot store the results for future use.
Implications of Knowledge Representation System on Businesses
Businesses derive several benefits from knowledge representation. Here are a few advantages you can receive while implementing and using knowledge representation.
We can create objects and relationships and reveal constraints within a problem.
Businesses can understand the influence of an object or other relations.
Using knowledge representation, inferences can be easily made using the objects and relations.
Remove or hide components that are not related to the issues that are solved.
Transparency in problems solved gives businesses a grip on the issues solved.
Helps uncover an issue comprehensively, and therefore, issues can be solved effectively.
Helps finish tasks faster through accurate procedures.
Final Thoughts
Today, AI-enabled machines implement things in the most adaptable and intelligent ways, just like humans do. AI is relentlessly evolving each and every day, as we can see in the most ideal manner to help resolve complex issues with the least human interference.
Indeed, there are several tools that enable easy implementation of tasks and quick resolution of issues. Indeed, knowledge representation is one of the key attributes that AI-powered machines use to work smart to produce the best outcomes.
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