Tag: Knowledge Representation
Knowledge Representation in AI and Its Business Significance
Artificial Intelligence is the most innovative technology that has always captivated human beings. Robots that can think, act, decode complex information, and make smart decisions continue to inspire tech enthusiasts, sci-fi novels, and movie makers.
Humans know things, which we term knowledge, and all our abilities to perform various actions in the real world originates from the knowledge we have gathered so far.
If we were to make Artificial Intelligence programs more sophisticated and capable of imitating human intelligence in a given scenario, we would be required to feed the AI systems with more and often complex information about the real-world. Now that leads us to the concept of Knowledge Representation in Artificial Intelligence.
What is Knowledge Representation?
Knowledge representation is a field of artificial intelligence that allows AI programs to answer questions intelligently and make deductions about real-world facts. It refers to representing information about the world in a way that a computer system can understand and use it to solve real-life problems or handle real-life tasks.
Knowledge representation in AI concerned with how knowledge can be represented symbolically and manipulated in an automated way by reasoning programs. For instance, an AI software can be trained to solve complex tasks such as diagnosing a medical condition only if the relevant knowledge is made available to the AI system as required.
There are two primary concepts in Knowledge Representation:
#1Knowledge
Knowledge refers to the fact of knowing something that has been gained through an experience and learning which makes the agent (AI application like a chatbot) familiar.
In Artificial Intelligence, a machine will perform a specific action based on a specific condition only if it has gained an experience from the past. For example, an AI agent can solve a chessboard puzzle only if it has gained sufficient knowledge on how to solve the puzzle and win the game.
#2Representation
Representation refers to the process of representing what has been gained from the knowledge. Representation consists of the objectives that are used to express the knowledge that is required to solve a specific problem.
The different kinds of knowledge that need to be represented in AI systems include:
- Objects: All the information related to the objects present in our world. For example, buses need drivers, cars have wheels, guitars have strings, etc.
- Events: Numerous events that are taking place constantly in our world and the human perception of the events. For example, natural disasters, wars, achievements, etc.
- Performance: Deals with how humans react in various situations. Representing this knowledge is essential for the AI agent to understand the behavior side of knowledge.
- Facts: Knowledge based on the factual description of our world, such as the earth is not flat but also not an exact round.
- Meta Knowledge: Meta knowledge deals with the knowledge that we already know and allows AI to perceive the same.
- Knowledge Base: A knowledge base is a collection of information related to any discipline. For example, a knowledge base on road construction.
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Different Types of Knowledge Represented in AI
In a broad sense, knowledge is the awareness or familiarity gained by the experiences of facts, data, and situations. Following are the different types of knowledge that need to be represented in artificial intelligence.
Declarative Knowledge
Refers to the facts, objects, and concepts that allow us to describe the world around us. It shares the description of something expressed in declarative sentences which is simpler than procedural language.
Structural Knowledge
Constitutes the problem-solving knowledge that describes the relationship between various concepts or objects and their descriptions.
Procedural Knowledge
Also known as imperative knowledge, procedural knowledge is used to complete any task with specific rules, strategies, processes, or agendas. It’s the type of knowledge which is responsible for knowing how to do a particular task and hence relies on the task we are trying to finish.
Meta Knowledge
As mentioned above, meta knowledge refers to predefined knowledge about things that we are already aware of. This knowledge typically includes the study of tagging, planning, learning, etc.
Heuristic Knowledge
Also known as shallow knowledge, heuristic knowledge is highly used in the process of reasoning as it can solve issues based on the experiences of past problems. Thus, it provides a knowledge-based approach to define a problem and take action.
Four Fundamental Knowledge Representation Techniques in AI
In artificial intelligence, knowledge can be represented in different ways depending on the structure of the knowledge, perspective of the designer or even based on the type of internal structure used. An effective knowledge representation should be rich enough to include the knowledge required to solve the problem. It should be natural, compact and maintainable.
Here are the four fundamental knowledge representation techniques used in AI:
1. Logical Representation
Knowledge and logical reasoning play an integral role in artificial intelligence. However, you often require more than just general and powerful methods to ensure intelligent behavior. Formal logic is the most helpful tool in this area. It is a language with unambiguous representation guided by certain concrete rules.
Knowledge representation relies heavily not so much on what logic is used but on the method of logic used to understand or decode knowledge.
Logical representation technique allows designers to lay down certain vital communication rules to share and acquire information to and from agents with minimum errors in communication. Different rules of logic allow you to represent different things resulting in an efficient inference. Hence, the knowledge acquired by logical AI agents will be definite which means it will either be true or false.
Although working with logical representation is challenging, it forms the basis of most of the programming languages used currently and enables you to construct logical reasoning.
2. Semantic Network
A semantic network allows you to store knowledge in the form of a graphic network with nodes and arcs representing objects and their relationships. It could represent physical objects or concepts or even situations. A semantic network is generally used to represent data or reveal structure. It is also used to support conceptual editing and navigation.
A semantic network is simple and easy to implement and understand. It is more natural than logical representation. Semantic network allows you to categorize objects in various forms and then link those objects. It also has greater expressiveness than logic representation.
3. Frame Representation
A frame is a collection of attributes and its associated values, which describes an entity in the real world. It is a record-like structure consisting of slots and their values. Slots could be of varying sizes and types. These slots have names and values. Or they could have subfields named as facets that allow you to put constraints on the frames.
There is no restraint or limit on the value of facets a slot could have, or the number of facets a slot could have or the number of slots a frame could have. Since a single frame is not very useful, building a frame system by collecting frames that are connected to each other will be more beneficial. It’s a flexible knowledge representation technique used by various AI applications.
4. Production Rules
Production rule-based representation has many properties essential for knowledge representation. It consists of production rules, working memory, and recognize-act-cycle. It is also called condition-action rules. If the condition of a rule is true according to the current database, the action associated with the rule is performed.
Although production rules lack precise semantics for the rules and are not always efficient, the rules lead to a higher degree of modularity. And it is the most expressive knowledge representation system.
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Cycle of Knowledge Representation in AI
When building an AI system, it’s important to come up with a knowledge representation system that will help in feeding the AI system with the essential knowledge. The AI knowledge cycle consists of some major components to demonstrate intelligent behavior that make knowledge representation possible.
- Perception: With the help of this component, the AI system can retrieve information from the environment through audio, video, text, time, or any other form of sensory input. Data derived from various sensors will make the AI system familiar with its environment and help in interacting with it.
- Learning: The knowledge gained will equip the AI system to run the deep learning algorithms that are pre-written to make the AI system transfer the needed information from the perception component to the learning component for better learning and understanding.
- Knowledge Representation and Reasoning: These are the most important components of the AI knowledge cycle that help demonstrate human-like intelligence in machines. The goal of these components is to understand and build intelligent behavior by focusing on what an AI agent needs to know in order to behave intelligently. The KRR components go through the knowledge data of AI systems and find the relevant knowledge to be provided to the learning model.
- Planning and Execution: These are independent components that work in tandem with the knowledge and reasoning blocks. These blocks collect information from the knowledge and reasoning blocks to plan and execute certain actions depending on the analysis of knowledge representation and reasoning.
Approaches to Knowledge Representation in AI
There are four main approaches to knowledge representation in artificial intelligence. Each approach corresponds to a technique used to represent the knowledge discussed above.
1. Simple Relational Knowledge
- This approach is the simplest way of storing facts which uses the relational method.
- In this method, each fact related to a set of the object is laid out systematically in columns.
- This approach is widely used in DBMS (database management systems) to represent the relationship between different entities.
- One drawback of this approach is that it limits the opportunity for inference.
2. Inheritable Knowledge
- In this approach, all the data should be stored in a hierarchy of classes, arranged in a generalized form or a hierarchical manner.
- This approach helps apply inheritance property in order to gain inheritable knowledge.
- Unlike the Simple Relation method, this approach helps us to identify the relations between instance and class.
- All the objects are represented as nodes in this approach.
3. Inferential Knowledge
- Inferential knowledge is a formal approach that allows us to retrieve facts with a high level of accuracy.
- This approach represents knowledge in the form of formal logics with correctness guaranteed.
4. Procedural Knowledge
- Procedural knowledge approach uses small programs and codes such as simple if-then rules that describe how to do specific things or how to proceed with a specific task.
- Some of the popular coding languages used in this approach are LISP language and Prolog language.
- Though this approach is not capable of representing all cases, it’s highly useful in representing or storing heuristic or domain-specific knowledge.
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Properties of a Good Knowledge Representation System
A good knowledge representation system should meet the following requirements:
Representational adequacy
The knowledge representation (KR) system should be capable of representing each kind of required knowledge in a way the AI system can understand.
Inferential adequacy
The KR system should be flexible enough to manipulate existing knowledge to make way for new knowledge corresponding to the present structure.
Inferential efficiency
Inferential efficiency refers to the ability of the KR system to direct the inferential knowledge mechanism toward the most productive directions using appropriate guides.
Acquisitional efficiency
Acquisitional efficiency is the ability of the knowledge representation system to automatically acquire new knowledge, integrate the new information into the existing knowledge base, and use the same to improve efficiency and productivity.
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Why is Knowledge Representation Important for AI Systems?
Knowledge representation equips AI agents with the capabilities to solve the most complex tasks based on what they have learned from the knowledge given to them. The knowledge given to them could be human experiences, problem-solutions, if-then rules, response to specific scenarios, etc. which are represented in a specific way for the AI agent to understand and learn.
Knowledge representation is the technique that runs behind several technologies and machines that are around us. An AI software development can solve complex problems or complete difficult tasks successfully only by relying on a knowledge base which describes how to approach and perform each task.
Knowledge representation is a field of AI concerned with understanding, designing, and implementing ways of representing information in a comprehensible manner for the machines, so the AI programs can use this information to:
- Derive information that is implied by the AI agent,
- Communicate with people in natural language,
- Decide what to do next,
- Plan future activities, and
- Solve problems in areas that normally require human expertise.
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Benefits of Knowledge Representation in AI
Knowledge representation is the driving force that equips your AI program to support you in improving productivity, increasing competitive advantage, and minimizing risks and errors. Knowledge representation in AI delivers several benefits.
- Simplifies knowledge discovery in your business by capturing, storing, and retrieving knowledge through techniques like semantic search, natural language processing, and machine learning.
- Allows organizations to connect and combine knowledge across multiple processes and consolidate data from disparate sources.
- Keeps your company’s knowledge base up-to-date by removing unwanted and outdated information and prompts your workforce to update knowledge (data) regularly in the systems.
- Makes it easy to track your organization’s performance and knowledge management metrics such as individual and team performance, first call resolution rate, average call abandonment rate, average turnaround time, and so on.
- Helps in gathering feedback and recommendations from your employees, customers, partners, and community users to continuously update and improve your products and services.
- Ensures that all your employees are on the same page and are working with the same information to deliver a consistent customer experience.
- Allows you to extract insights and patterns from large sets of data in order to make predictions and provide your users with relevant and real-time information required to address their specific needs.
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How Can Fingent Help
Knowledge representation is the key to designing AI agents that can think and act smart while ensuring that such thinking can constructively contribute to their behavior and allow them to respond effectively to each scenario. However, it is important to choose the right type of knowledge representation if you want to ensure business success with AI.
There are certain considerations to keep in mind when designing a knowledge representation system. Factors such as the structure for storing knowledge, depth of information required for representing a subject adequately, etc. are some of the common dilemmas faced during the creation of knowledge systems.
At Fingent top custom software development company, we help you build custom AI applications that are well-tested and configured to process specialized data sets in order to produce expected results. Our AI experts can collaborate with you and help provide different types of knowledge to your AI systems to make your AI applications more competent and sophisticated.
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Frequently Asked Questions
Knowledge representation is a subfield of artificial intelligence that solely focuses on representing information about the real world around us in a way that computers can understand. For instance, knowledge representation takes all the concepts in a domain, describes how these concepts link to each other, and defines the rules that govern the behavior of the AI system based on each condition.
Following are the different types of knowledge representation used in artificial intelligence.
- >> Declarative Knowledge: Constitutes the facts, objects, and concepts that allow us to describe the world around us.
- >> Structural Knowledge: Includes the problem-solving knowledge that describes the relationship between various concepts or objects and their descriptions.
- >> Procedural Knowledge: Used to complete any task with specific rules, strategies, processes, or agendas.
- >> Meta Knowledge: Constitutes the predefined knowledge about things that we are already aware of.
- >> Heuristic Knowledge: Used in the process of reasoning as it can solve issues based on the experiences of past problems.
AI agents can express intelligent behavior only if they acquire the knowledge and experiences that we, humans, have gained from the real world around us. Knowledge representation allows AI programs to use the information they’ve learned to:
- >> Derive information that is implied by the AI agent,
- >> Communicate with people in natural language,
- >> Decide what to do next,
- >> Plan future activities, and
- >> Solve problems in areas that normally require human expertise.
It’s important to choose a knowledge representation system with the following characteristics:
- >> KR system should be extensive, well-represented, and easily decipherable
- >> Should cover a wide range of standard computing procedures to support large scale application
- >> Must be easy to access and provide the options to identify events and decode the reaction of different components
The AI knowledge cycle comprises multiple elements or entities that are used to represent and utilize knowledge. These entities include perception, learning, knowledge, reasoning, planning, and execution.
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