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Knowledge Representation in AI - Types, Issues, & Techniques

Knowledge Representation in AI - Overview

Artificial intelligence (AI) is based on the core idea of knowledge representation, which tries to capture and organize knowledge in a meaningful and structured fashion. It entails archiving data and making it available to AI systems so they may learn, reason, and make decisions based on knowledge. In this thorough book, Learn Artificial Intelligence , we will delve into the complexities of knowledge representation in AI, looking at its different forms, the knowledge cycle, approaches, strategies, benefits, drawbacks, practical applications, difficulties, and future directions.

What is Knowledge Representation in AI?

The process of encoding information in a way that an AI system can comprehend and use is known as knowledge representation in AI. It entails converting information and ideas from the real world into a form that computers can use, analyze, and make conclusions from. AI systems may imitate human cognitive functions including problem-solving, decision-making, and language comprehension thanks to this representation.

Types of Knowledge in AI

Knowledge in AI can be divided into various types of knowledge in AI, each of which serves a particular function in the process of knowledge representation as a whole.

1.) Declarative Knowledge

Declarative knowledge is the representation of information, facts, and claims about the outside world. Without outlining the method of knowledge acquisition or application, it concentrates on what is true or incorrect. Declarative knowledge is frequently modeled using logic-based formalisms and serves as the basis for other types of knowledge.

2.) Procedural Knowledge

Imperative knowledge, usually referred to as procedural knowledge, specifies how to carry out tasks or actions. It contains detailed guidelines, techniques, and rules that assist AI systems in carrying out particular tasks. AI systems need procedural knowledge to effectively complete complicated tasks and address issues.

3.) Meta Knowledge

Also known as knowledge about knowledge, meta-knowledge is the study of the composition, arrangement, and characteristics of knowledge. It aids AI systems in managing and making sense of their own information, allowing them to adjust, learn, and develop over time.

The Knowledge Cycle in AI

The continual process of obtaining, representing, reasoning, & updating knowledge within an AI system is referred to as the "knowledge cycle" in AI.

The Knowledge Cycle in AI

The following stages make up this process:

  • Knowledge Acquisition: Information is gathered from a variety of sources, including databases, documents, experts, and even other AI systems. The goal of this stage is to gather pertinent knowledge and convert it into an appropriate representation format.
  • Knowledge Representation: The key stage of the knowledge cycle is knowledge representation, where acquired information is organized and encoded in a language that AI systems can comprehend and use. The effectiveness and efficiency of knowledge processing are greatly influenced by the representation approach chosen.
  • Knowledge Reasoning: During this phase, AI systems use the knowledge that has been encoded to carry out reasoning tasks like inference, deduction, or induction. AI systems can reason to create new knowledge from existing knowledge and to make defensible decisions based on the information at hand.
  • Knowledge Update: The knowledge representation has to be updated when new information becomes available or as outdated knowledge is amended or rendered invalid. This phase guarantees that AI systems are current and flexible enough to respond to changing conditions.

Approaches to Knowledge Representation in AI

AI uses a variety of Approaches to knowledge representation, each with unique advantages and disadvantages. Several of the frequently used Approaches to knowledge representation include:

  • Logic-Based Methods: Knowledge is represented using formal logic in logic-based techniques, such as propositional logic, first-order logic, or fuzzy logic. These methods enable exact and rigorous representation, allowing AI systems to carry out logical inference and reasoning.
  • Semantic Networks: Semantic networks are ways to describe knowledge by connecting labeled edges (relationships) to nodes (concepts). With this method, knowledge graphs may be navigated and explored by AI systems and emphasize the connections between concepts.
  • Frames or scripts: Frames and scripts capture the characteristics, traits, and behaviors of things or circumstances and convey knowledge as organized frames or templates. This method promotes reasoning based on common scenarios and makes it easier to convey complex knowledge.
  • Ontologies: Ontologies define concepts, relationships, and limitations within a certain domain to offer a formal representation of knowledge. They give AI systems the ability to reason and infer using domain-specific information, improving accuracy and understanding of the context.

Techniques of Knowledge Representation in AI

There are other techniques of Knowledge Representation in addition to the different approaches.

  • Rule-Based Systems: Using a set of rules, or production rules, rule-based systems express knowledge. AI systems may make judgments based on certain conditions thanks to these rules, which are made up of conditions and actions.
  • Semantic Web Technologies: Semantic web technologies like the Resource Description Framework (RDF) and the Web Ontology Language (OWL) offer standardized formats for expressing and transferring knowledge on the web. These tools make it possible for AI systems to access and incorporate data from many sources.
  • Neural Networks: Through the training process, neural networks, in particular deep learning models, can learn and implicitly represent knowledge. These models are excellent at identifying intricate patterns and connections in unstructured data, including speech, texts, and photographs.
  • Statistical Models: Using probabilistic linkages and statistical inference, statistical models, such as Bayesian networks or Markov models, express knowledge. These models give AI systems the ability to reason and decide in the face of uncertainty.

Advantages and Limitations of Different Techniques of Knowledge Representation

Each method of knowledge representation has unique benefits and drawbacks that make it suitable for various AI applications . Several of the strategies discussed above have the following advantages and limitations:

Logic-Based Methods

  • Formal and accurate representation.
  • Facilitates deduction and logical reasoning.

Limitations

  • Difficulty dealing with uncertainty and insufficient data.
  • limited scalability for huge knowledge sets.

Semantic networks

  • Emphasises linkages and interactions between concepts.
  • Allows for flexible knowledge graph traversal.
  • Difficulties in modeling sophisticated knowledge structures.
  • Absence of formal semantics and reasoning abilities.

Scripts and frames

  • Records specific characteristics and actions of things or circumstances.
  • Backs up logic with common scenarios.
  • Demands the explicit description of all potential circumstances.
  • Minimal generalization outside of established frames or scripts.
  • Supports reasoning & inference based on domain constraints.
  • Offers a formal representation of knowledge relevant to a domain.
  • Ontology construction requires topic specialists.
  • Maintaining and updating them as knowledge expands presents difficulties.

Real-World Applications of Knowledge Representation in AI

AI's knowledge representation is used in a variety of fields and industries.

Here are a few significant real-world examples:

  • Expert Systems:  To simulate human competence and offer wise decision support, expert systems in AI make use of knowledge representation techniques. They are commonly utilized in industries like engineering, finance, and medicine where expertise in a certain topic is essential.
  • Natural Language Processing: The ability of AI systems to comprehend, decipher, and produce human language is made possible by knowledge representation, which is a key component of NLP applications. Applications for NLP include sentiment analysis, language translation, chatbots, and virtual assistants.
  • Robotics and autonomous systems: Knowledge representation helps with perception, planning, & decision-making in these systems. It enables robots to comprehend their surroundings, gain knowledge from their past, and efficiently communicate with people.
  • Recommender Systems: Recommender systems employ knowledge representation to model item features, comprehend user preferences, and generate tailored recommendations. They are widely utilized in content recommendation engines, music and video streaming platforms, and e-commerce.

Challenges and Future Directions in Knowledge Representation in AI

Several obstacles and potential directions still exist in knowledge representation, despite the substantial progress made in this area. Among the principal difficulties are:

  • Scalability: Scalability becomes a significant difficulty as knowledge's volume and complexity rise. Large knowledge bases must be efficiently represented and processed using sophisticated methods and distributed computing concepts.
  • Information that is Uncertain or Incomplete: AI systems frequently work with information that is uncertain or incomplete. A major research area is improving knowledge representation approaches to manage uncertainty and reason with inadequate data.
  • Knowledge Fusion and Integration: Combining and integrating knowledge from various sources and modalities is a difficult task. The goal of future research is to create methods that make it possible for heterogeneous knowledge to be seamlessly integrated for better AI performance.
  • Explainability & Interpretability: AI systems should be able to justify their decisions with explanations. Building trust, assuring ethical AI, and satisfying legal standards all depend on the development of clear and understandable knowledge representation approaches.

Best Practices of Knowledge Representation in AI

AI knowledge representation entails gathering and arranging data for computational use. Top practices consist of:

  • Expressivity: Use a representation language that fully expresses the constraints, relationships, supporting concepts, and domain knowledge.
  • Formality: Offer well-defined syntax and semantics for defensible inference and automated reasoning.
  • Ontology: Create an ontology to specify domain concepts, entities, and relationships, creating consensus.
  • Modularity: In order to facilitate maintenance, reuse, and scalability, break complex knowledge down into modules.
  • Granularity: Represent knowledge at the proper level of detail to facilitate sound deliberation and judgment.
  • Probabilistic reasoning and uncertainty: Bayesian networks and fuzzy logic are two tools for dealing with uncertainty.
  • Logic and inference: Based on the knowledge type and problem domain, choose the best logical processes.
  • Scalability: Create the system with the ability to efficiently manage vast knowledge bases, facilitating quick retrieval and inference.
  • Integration with Learning: Include learning algorithms as well as fresh information gleaned from data.
  • Evaluation and iteration: Based on input and performance indicators, continuously assess and improve the representation.

Resources for further learning and practice

Online tutorials and courses: Explore Additional Learning and Practice Resources with us at https://www.scholarhat.com/training/artificial-intelligence-certification-training . There are numerous online tutorials and courses available that specifically focus on Artificial Intelligence.

Artificial intelligence's foundation is knowledge representation, which enables AI systems to comprehend, reason, and arrive at defensible decisions. We examined all facets of knowledge representation in AI in this thorough guide, from its definition to its types, methodologies, strategies, benefits, drawbacks, applications, difficulties, and future directions. Unlocking the potential of knowledge representation will be essential for creating intelligent systems that can solve difficult issues and supplement human abilities as AI develops.

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Techniques of Knowledge Representation

Artificial Intelligence (AI) is concerned with developing computer programs that can perform tasks that require human intelligence. One of the essential aspects of AI is knowledge representation, which involves capturing and storing human knowledge in a way that machines can understand and use. Knowledge representation is crucial in AI because it helps machines reason, make decisions, and solve problems like humans.

In this article, we will explore the different knowledge representation techniques in AI, including logical representation, semantic network representation, frame representation, and production rules. We will also discuss the different types of knowledge that need to be represented in AI, the cycle of knowledge representation in AI and the relationship between knowledge and intelligence.

Introduction

AI aims to replicate human intelligence in machines to perform complex tasks, including perception, reasoning, decision-making, and problem-solving. However, achieving this goal requires machines to have access to human knowledge and use it to perform these tasks. Knowledge representation, which refers to the techniques of capturing and storing human knowledge in a way that machines can understand and use, is a critical component of AI. The main challenge in knowledge representation is finding a way to represent knowledge that is understandable by machines and can be used for reasoning and problem-solving.

To address this challenge, various techniques of knowledge representation in AI have been developed, such as rule-based systems, semantic networks, frames, ontologies, and logic-based representations. These techniques provide a structured way to represent knowledge and enable machines to reason about it and perform complex tasks.

What is Knowledge Representation?

Knowledge representation is the process of representing information in a structured form that is easily understood by both humans and machines. It is a fundamental task in artificial intelligence (AI) and computer science that involves organizing knowledge into a form that can be used for reasoning, problem-solving, and decision-making.

The goal of knowledge representation is to make explicit the relationships between concepts, ideas, and objects in a way that can be used to make inferences and draw conclusions. To achieve this, various knowledge representation techniques can be used, such as logical representation, semantic network representation, frame representation, and production rules.

In practical terms, with the help of techniques of knowledge representation AI is used to build intelligent systems that can understand natural language, recognize patterns, learn from data, and make predictions. For example, a knowledge representation system might be used to build a chatbot that can answer questions about a particular topic or a recommendation engine that can suggest products based on a user's preferences.

The Different Kinds of Knowledge that Need to be Represented in AI

The knowledge that needs to be represented in AI can be classified into several categories, including objects, events, performance, facts, meta-knowledge, and knowledge-base.

Objects refer to things in the world that have physical properties and can be observed, touched, or manipulated. Examples of objects include cars, buildings, and people. Object-oriented programming is an example of a technique that uses objects to represent knowledge in AI.

Events refer to actions or occurrences that take place in the world. Examples of events include driving a car, cooking food, or attending a concert. Event-based systems use events to represent knowledge in AI.

Performance

Performance refers to the behavior of agents or systems that perform a task. It includes the goals and objectives of the task and the criteria used to evaluate performance. Performance-based systems use performance to represent knowledge in AI.

Facts refer to propositions that are either true or false. They are statements that can be verified using evidence or logical deduction. Examples of facts include "the sky is blue," "the earth revolves around the sun," and "water boils at 100 degrees Celsius." Knowledge-based systems use facts to represent knowledge in AI.

Meta-Knowledge

Meta-knowledge refers to knowledge about knowledge. It includes information about the structure and organization of knowledge, the sources of knowledge, and the reliability and validity of knowledge. Meta-knowledge is essential in AI because it helps machines reason about the quality and validity of the knowledge they are using.

Knowledge-Base

A knowledge base is a collection of knowledge that is organized and stored in a way that machines can access and use it. It includes facts, rules, procedures, and other knowledge relevant to a particular domain. Knowledge-based systems use a knowledge base to represent knowledge in AI.

Techniques of Knowledge Representation in AI

There are several knowledge representation techniques in AI, including logical representation, semantic network representation, frame representation, and production rules. Each of these techniques has its syntax and semantics, advantages, and disadvantages.

Logical Representation

Logical Representation is a fundamental method of communicating knowledge to machines through a well-defined syntax with precise rules. This syntax should be unambiguous and able to handle prepositions, making it an ideal way to represent facts. There are two types of logical representation: Propositional Logic and First-order Logic.

Propositional Logic , also known as propositional calculus or statement logic, is a formal system of logic that deals with the relationships between propositions, which are statements that are either true or false. Propositional logic is based on the Boolean system, which means that propositions are evaluated as either true or false. In propositional logic, propositions are combined using logical connectives such as "and", "or", and "not", and the resulting compound propositions can also be evaluated as true or false based on the truth values of their component propositions.

First-order logic (FOL) , also known as first-order predicate calculus (FOPC) or first-order logic with identity, is an extension of propositional logic that allows for the representation of more complex relationships between objects. In FOL, propositions are constructed using predicates, which are statements that describe properties or relations between objects, and quantifiers, which specify the scope of the variables in the proposition.

FOL allows for more precise and flexible reasoning about the relationships between objects and is widely used in mathematics, computer science, and philosophy.

In logic, we use symbols and operators to represent concepts like truth, negation, conjunction, disjunction, implication, quantification, and identity. There are different types of logical representation like propositional logic, first-order logic, and higher-order logic.

The semantics of logical representation involves assigning meaning to these symbols and formulas. This is done by defining a set of axioms and rules for manipulating these symbols.

There are several advantages to using logical representation, such as its ability to facilitate logical reasoning and serve as the foundation for programming languages. However, there are also some limitations and challenges associated with this method. One disadvantage is that logical representations can be restrictive and difficult to work with. Additionally, this approach may not be very intuitive, and the process of inference may not always be efficient.

  • It is Monday.
  • The Sun rises from the North (False proposition)
  • 3+3= 8(False proposition)
  • 7 is a prime number.

Semantic Network Representation

A semantic network is a graphical representation of knowledge, where nodes represent concepts or objects, and links represent relationships between them. The syntax of a semantic network consists of nodes and links, and the semantics involve defining the meaning of each node and link.

One of the main advantages of semantic networks is that they can be easily visualized, making them more intuitive to understand than logical representations. Additionally, they can categorize objects and link them together.

However, there are some drawbacks associated with this representation method. For instance, semantic networks can be computationally expensive at runtime, as traversing the entire network tree may be necessary to answer certain questions. Furthermore, modeling the vastness of human-like memory is not practical. Semantic networks also lack quantifier equivalents such as "for all" or "for some", and do not have standard definitions for link names. Additionally, they are not inherently intelligent and depend on the creator of the system.

Example: The following are a few statements that must be represented with nodes and arcs:

  • Jerry is a cat.
  • Jerry is a mammal
  • Jerry is owned by Priya.
  • Jerry is brown-colored.
  • All Mammals are animals.

semantic network representation

Frame Representation

Frame representation is a technique for organizing knowledge in a hierarchical structure. A frame is a structured record that describes an entity in the world by using a collection of attributes and their corresponding values. In artificial intelligence, frames serve as a data structure that divides knowledge into substructures by representing typical situations.

The syntax of a frame consists of attributes and values, and the semantics involve defining the meaning of each attribute and value. The frame representation method offers several advantages in the field of artificial intelligence. One of its key strengths is its ability to simplify programming by grouping related data. It is also a highly flexible approach utilized in various AI applications. Moreover, the visual nature of frame representation makes it easy to comprehend.

However, there are also some limitations associated with frame representation. For instance, the inference mechanism in frame systems can be challenging to process, and the approach is not always the most efficient. Additionally, the generalized nature of frame representation means that it may not always be the best fit for more specific or complex scenarios.

Example: Consider the example of a book frame.

Production Rules

Production rules are a set of IF-THEN statements that represent knowledge. The IF part of a rule is a condition, and the THEN part is an action to be taken if the condition is met. Production rules can be used to represent a wide range of knowledge, including facts, procedures, and heuristics.

The production rules system is composed of three key components:

  • The set of production rules
  • The working memory
  • The recognize-act-cycle

The syntax of a production rule consists of IF-THEN statements, and the semantics involve defining the meaning of the conditions and actions. One advantage of this system is that production rules can be expressed in natural language, which makes them easier to understand and modify. Additionally, the modularity of the production rules system allows for easy removal or modification of individual rules.

However, there are also some drawbacks to the production rules system. They do not possess any learning capabilities and cannot store the result of a problem for future use. Furthermore, they can become complex and difficult to maintain as the number of rules increases.

  • IF (at auto-rickshaw stop AND rickshaw arrives) THEN action (get into the rickshaw)
  • IF (in the rickshaw AND paid AND empty seat) THEN action (sit down).
  • IF (in rickshaw AND unpaid) THEN action (pay charges).
  • IF (rickshaw arrives at destination) THEN action (get down from the rickshaw).

Cycle of Knowledge Representation in AI

The cycle of knowledge representation in AI consists of five stages: perception, learning, knowledge representation, reasoning, planning, and execution.

Perception is the process of acquiring information from the environment through sensors. This information is then processed and interpreted to generate knowledge.

Learning is the process of acquiring new knowledge from experience. This can be achieved through supervised learning, unsupervised learning, or reinforcement learning.

Knowledge Representation & Reasoning

Knowledge representation and reasoning is the stage where acquired knowledge is transformed into a form that can be processed by machines. This involves choosing an appropriate KR technique and representing knowledge using that technique. Reasoning involves using the knowledge represented to draw inferences and make decisions.

Planning is the stage where the system uses the acquired knowledge and reasoning to generate a sequence of actions to achieve a particular goal. This involves selecting the most appropriate actions to achieve the goal while taking into account any constraints or limitations.

Execution is the final stage where the system performs the planned actions. The success of the execution depends on the accuracy and completeness of the knowledge representation, reasoning, and planning.

What is the Relation between Knowledge & Intelligence?

Knowledge and intelligence are closely related concepts, but they are not the same thing. Knowledge is information that is acquired through experience or education. Intelligence is the ability to learn, reason, and solve problems. Knowledge is necessary for intelligence, but it is not sufficient.

Intelligence involves the ability to use the techniques of knowledge representation flexibly and adaptively. This requires not only acquiring knowledge but also being able to reason with it, apply it to new situations, and use it to solve problems. The ability to learn from experience and adapt to new situations is a key aspect of intelligence.

For instance, techniques of knowledge representation in AI can help AI systems learn and reason about language, enabling them to communicate effectively with humans. They can also be used to represent knowledge about a particular domain, such as medicine or finance, allowing AI systems to reason about complex problems in those domains.

However, techniques of knowledge representation in AI alone are not sufficient to create truly intelligent AI systems. Intelligence also involves the ability to learn from experience and adapt to new situations, which requires the use of machine learning algorithms and other advanced AI techniques.

  • Knowledge representation is a critical component of AI that enables machines to reason about the world in a way that is similar to how humans reason.
  • Various knowledge representation techniques in AIsuch as logical representation, semantic network representation, frame representation, and production rules can be used to represent knowledge.
  • The cycle of knowledge representation in AI involves perception, learning, knowledge representation, reasoning, planning, and execution, all of which rely on the use of techniques of knowledge representation.
  • The relationship between knowledge and intelligence is that knowledge is necessary for intelligence, but intelligence requires more than just knowledge.
  • Overall, advances in techniques of knowledge representation in AI and AI algorithms have the potential to revolutionize many fields, from medicine and finance to transportation and education.

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Artificial Intelligence

What is knowledge representation in ai techniques you need to know.

what is knowledge representation techniques

Human beings are good at understanding, reasoning and interpreting knowledge. And using this knowledge, they are able to perform various actions in the real world. But how do machines perform the same? In this article, we will learn about Knowledge Representation in AI and how it helps the machines perform reasoning and interpretation using Artificial Intelligence in the following sequence:

What is Knowledge Representation?

Different types of knowledge.

  • Cycle of Knowledge Representation
  • What is the relation between Knowledge & Intelligence?
  • Techniques of Knowledge Representation

Representation Requirements

  • Approaches to Knowledge Representation with Example

Knowledge Representation in AI describes the representation of knowledge. Basically, it is a study of how the beliefs, intentions , and judgments of an intelligent agent can be expressed suitably for automated reasoning. One of the primary purposes of Knowledge Representation includes modeling intelligent behavior for an agent.

Knowledge Representation and Reasoning ( KR, KRR ) represents information from the real world for a computer to understand and then utilize this knowledge to solve complex real-life problems like communicating with human beings in natural language. Knowledge representation in AI is not just about storing data in a database, it allows a machine to learn from that knowledge and behave intelligently like a human being.

The different kinds of knowledge that need to be represented in AI include:

  • Performance
  • Meta-Knowledge
  • Knowledge-base

Now that you know about Knowledge representation in AI, let’s move on and know about the different types of Knowledge.

There are 5 types of Knowledge such as:

Declarative Knowledge – It includes concepts, facts, and objects and expressed in a declarative sentence.

Structural Knowledge – It is a basic problem-solving knowledge that describes the relationship between concepts and objects.

Procedural Knowledge – This is responsible for knowing how to do something and includes rules, strategies, procedures, etc.

Meta Knowledge – Meta Knowledge defines knowledge about other types of Knowledge.

Heuristic Knowledge – This represents some expert knowledge in the field or subject.

These are the important types of Knowledge Representation in AI. Now, let’s have a look at the cycle of knowledge representation and how it works.

Cycle of Knowledge Representation in AI

Artificial Intelligent Systems usually consist of various components to display their intelligent behavior. Some of these components include:

  • Knowledge Representation & Reasoning

Here is an example to show the different components of the system and how it works:

The above diagram shows the interaction of an AI system with the real world and the components involved in showing intelligence.

  • The Perception component retrieves data or information from the environment. with the help of this component, you can retrieve data from the environment, find out the source of noises and check if the AI was damaged by anything. Also, it defines how to respond when any sense has been detected.
  • Then, there is the Learning Component that learns from the captured data by the perception component. The goal is to build computers that can be taught instead of programming them. Learning focuses on the process of self-improvement. In order to learn new things, the system requires knowledge acquisition, inference, acquisition of heuristics, faster searches, etc.
  • The main component in the cycle is Knowledge Representation and Reasoning which shows the human-like intelligence in the machines. Knowledge representation is all about understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top-down and focus on what an agent needs to know in order to behave intelligently. Also, it defines how automated reasoning procedures can make this knowledge available as needed.
  • The Planning and Execution components depend on the analysis of knowledge representation and reasoning. Here, planning includes giving an initial state, finding their preconditions and effects, and a sequence of actions to achieve a state in which a particular goal holds. Now once the planning is completed, the final stage is the execution of the entire process.

So, these are the different components of the cycle of Knowledge Representation in AI. Now, let’s understand the relationship between knowledge and intelligence.

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What is the relation between knowledge & intelligence.

In the real world, knowledge plays a vital role in intelligence as well as creating artificial intelligence . It demonstrates the intelligent behavior in AI agents or systems . It is possible for an agent or system to act accurately on some input only when it has the knowledge or experience about the input.

Let’s take an example to understand the relationship:

In this example, there is one decision-maker whose actions are justified by sensing the environment and using knowledge. But, if we remove the knowledge part here, it will not be able to display any intelligent behavior.

Now that you know the relationship between knowledge and intelligence, let’s move on to the techniques of Knowledge Representation in AI.

Techniques of Knowledge Representation in AI

There are four techniques of representing knowledge such as:

Now, let’s discuss these techniques in detail.

Logical Representation 

Logical representation is a language with some definite rules which deal with propositions and has no ambiguity in representation. It represents a conclusion based on various conditions and lays down some important communication rules . Also, it consists of precisely defined syntax and semantics which supports the sound inference. Each sentence can be translated into logics using syntax and semantics.

Advantages:

  • Logical representation helps to perform logical reasoning.
  • This representation is the basis for the programming languages.

Disadvantages:

  • Logical representations have some restrictions and are challenging to work with.
  • This technique may not be very natural, and inference may not be very efficient.

Semantic Network Representation

Semantic networks work as an alternative of predicate logic for knowledge representation. In Semantic networks, you can represent your knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs which describe the relationship between those objects. Also, it categorizes the object in different forms and links those objects.

This representation consist of two types of relations:

  • IS-A relation (Inheritance)
  • Kind-of-relation
  • Semantic networks are a natural representation of knowledge.
  • Also, it conveys meaning in a transparent manner.
  • These networks are simple and easy to understand.
  • Semantic networks take more computational time at runtime.
  • Also, these are inadequate as they do not have any equivalent quantifiers.
  • These networks are not intelligent and depend on the creator of the system.

Frame Representation

A frame is a record like structure that consists of a collection of attributes and values to describe an entity in the world. These are the AI data structure that divides knowledge into substructures by representing stereotypes situations. Basically, it consists of a collection of slots and slot values of any type and size. Slots have names and values which are called facets.

  • It makes the programming easier by grouping the related data.
  • Frame representation is easy to understand and visualize.
  • It is very easy to add slots for new attributes and relations.
  • Also, it is easy to include default data and search for missing values.
  • In frame system inference, the mechanism cannot be easily processed.
  • The inference mechanism cannot be smoothly proceeded by frame representation.
  • It has a very generalized approach.

Production Rules

In production rules, agent checks for the condition and if the condition exists then production rule fires and corresponding action is carried out. The condition part of the rule determines which rule may be applied to a problem. Whereas, the action part carries out the associated problem-solving steps. This complete process is called a recognize-act cycle.

The production rules system consists of three main parts:

  • The set of production rules
  • Working Memory
  • The recognize-act-cycle

The production rules are expressed in natural language.

The production rules are highly modular and can be easily removed or modified.

It does not exhibit any learning capabilities and does not store the result of the problem for future uses.

During the execution of the program, many rules may be active. Thus, rule-based production systems are inefficient.

So, these were the important techniques for Knowledge Representation in AI. Now, let’s have a look at the requirements for these representations.

A good knowledge representation system must have properties such as:

Representational Accuracy: It should represent all kinds of required knowledge.

Inferential Adequacy : It should be able to manipulate the representational structures to produce new knowledge corresponding to the existing structure.

Inferential Efficiency : The ability to direct the inferential knowledge mechanism into the most productive directions by storing appropriate guides.

Acquisitional efficiency : The ability to acquire new knowledge easily using automatic methods.

Now, let’s have a look at some of the approaches to Knowledge Representation in AI along with different examples.

Approaches to Knowledge Representation in AI

There are different approaches to knowledge representation such as:

1. Simple Relational Knowledge

It is the simplest way of storing facts which uses the relational method. Here, all the facts about a set of the object are set out systematically in columns. Also, this approach of knowledge representation is famous in database systems where the relationship between different entities is represented. Thus, there is little opportunity for inference.

This is an example of representing simple relational knowledge.

2. Inheritable Knowledge

In the inheritable knowledge approach, all data must be stored into a hierarchy of classes and should be arranged in a generalized form or a hierarchal manner. Also, this approach contains inheritable knowledge which shows a relation between instance and class, and it is called instance relation. In this approach, objects and values are represented in Boxed nodes.

3. Inferential Knowledge

The inferential knowledge approach represents knowledge in the form of formal logic . Thus, it can be used to derive more facts. Also, it guarantees correctness.

Statement 1 : John is a cricketer.

Statement 2 : All cricketers are athletes.

Then it can be represented as;

Cricketer(John) ∀x = Cricketer (x) ———-> Athelete (x)s

These were some of the approaches to knowledge representation in AI along with examples. With this, we have come to the end of our article. I hope you understood what is Knowledge Representation in AI and its different types.

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What Is a Knowledge Representation?

  • Randall Davis
  • Howard Shrobe
  • Peter Szolovits

what is knowledge representation techniques

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  • Representation of Knowledge
  • Categories of knowledge
  • Types of Knowledge Representation
  • Requirements of knowledge Representation
  • Components of knowledge Representation system

What is Knowledge Representation in AI? | Techniques used in Knowledge Representation?

what is knowledge representation techniques

In the field of AI, there are many complex tasks required to evaluate either in the field of Machine learning or deep learning. It is indeed necessary to automate a knowledge processing system in such system. Knowledge representation is one such process which depends on the logical situation and enable a strategy to take a decision in acquiring knowledge. There are many types and levels of knowledge acquired by human in daily life but machines find difficult to interpret all types of knowledge. For such conditions, knowledge representation is used.

In knowledge representation algorithms, AI agents tend to think and they contribute in taking decisions. With the aid of such complex thinking, they are capable to solve the complex problems indulged in real world scenarios that are hard and time consuming for a human being to interpret.

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In AI systems, knowledge is represented in the following manner.

Events: Events are the occurrence of things in the real world. Anything which happens in real time are considered as the events. It is an important element as it is the initial thing to be considered in knowledge representation.

Objects are nothing but the facts that are actually true. Such facts can be habitual or a universal truth such as ‘The Sun rises in the East’, ‘Dogs are faithful’ or any facts which holds true in any events.

  • Meta-Knowledge: It is those knowledges which are already been acquired either by human brain or machine.
  • Knowledge base : It is the core component of the agents acquiring knowledge.
  • Performance: It describes about how good the knowledge is acquired and it can be applied to machines.

Knowledge can be categorized into two major types:

  • Tacit knowledge
  • Explicit knowledge

Tacit knowledge is the knowledge which exists within a human being. It does correspond to informal or implicit type of knowledge. It is quite difficult to articulate formally and is also difficult to communicate and share.

Explicit knowledge is the knowledge which exists outside a human being. It corresponds to formal type of knowledge. It is easier to articulate compared to tacit knowledge and is easier to share, store or even process.

Declarative Knowledge

Procedural knowledge, meta knowledge, heuristic knowledge, structural knowledge.

Below is the all types of Knowledge Representation with Examples

It is the segment of knowledge which stores factual information in a memory and it seem to be static in nature. These can be things or events or processes and the domain of such knowledge find the relation between events or things.

This knowledge is less general compared to declarative knowledge and is also known called imperative knowledge. It can have the potential to declare the accomplishment of a particular thing. It is generally used by modern mobile robots where they can be planned to attack into a building or perform navigation in a room. If we consider declarative knowledge implanted into a modern robot, they it will be assigned just a map instead of detailed plan of attack into a building.

In the field of AI, the knowledge of pre-defined knowledge is known as meta knowledge. A study of planning, tagging and learning are some of the examples of meta knowledge. This model tends to change with time and utilize a different specification. A knowledge engineer may utilize different forms of meta-knowledge given below:

Accuracy, Applicability, Assessment, Consistency, Completeness, Disambiguation, Justification, Life Span, Purpose, Source, Reliability.

This knowledge is also known as Shallow knowledge and it follows the principle of thumb rule. It is very efficient in reasoning process as it solves the problems based on the records of past problems or the problems which are compiled by experts. It provides knowledge based on the experiences it gathered during the past problems.

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This is the most basic knowledge used and applied in problem solving.  It tries to find out a relationship between concepts and objects.

Let us describe a relationship of the knowledge along with a flowchart.

what is knowledge representation techniques

As we can see, declarative knowledge is represented as describing one and procedural knowledge is represented as doing one. Now one more inference is, declarative knowledge is termed as explicit while procedural knowledge is termed as tacit.  If the knowledge can be articulated, it is a declarative knowledge and if cannot be articulated, it is known as procedural knowledge.

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Knowledge representation often provides information about those things which occur very common and make a pattern. Such patterned description is known as schemas. There are various types of schema which are

  • Frames – They contain information of all the attributes present in a given object. As for example, the description of a girl includes hair, facial pattern, eyes, etc. are considered as the frames. They represent knowledge of concepts and objects.
  • Scripts- They are often used to explain series of events which follow a sequence. For example, a hotel scene. They represent knowledge of events.
  • Stereotypes – They used to describe the characteristics present in a people.
  • Rule models- In a production system, they describe features which are shared commonly among a set of rules.

A knowledge representation has the following requirements

  • It should have the adequacy or fulfillment to represent all types of knowledge present in the domain.  It is also known as representational adequacy.
  • It should be capable enough to manipulate the representational structure in order to derive new structures which also should be corresponding to the new knowledge extracted from the old. It is also referred as inferential adequacy.
  • It should be able to indulge the additional information into the knowledge structure which can be further used to focus on inference mechanisms in the best possible direction. It is sometimes known as inferential efficiency.
  • It should acquire new knowledge with the help of automatic methods rather than relying on human source. This process is known as acquisitional efficiency.

The knowledge representation function contains the following components.

  • KR and Reasoning
  • Planning and Execution

Perception helps in extracting the information and can be helpful in telling us the status of AI system. It can detect any irregularity in the system and make us ready to decide whether an AI system has the potentiality of damage or not.

 Learning component captures the data which are already sensed by the perception component. Learning component tries to enable the computer to learn just like human instead of always programming it. This component solely tries to focus on how to self-improve the AI system.

KR and reasoning are used in AI to acquire knowledge in the smartest way. It focuses on the behavior of an AI agent and make sure that it more or less behaves like human. It is used to formalize the knowledge in the knowledge base.

Planning and execution try to find the optimal solution of the current state and tries to understand the impact of the same. Now it tries to seek out the solution that the final state holds and then it will try to terminate the entire process with a solution here itself.

The below diagram shows how the process of knowledge representation works.

what is knowledge representation techniques

Different approaches are used by knowledge representation system. Those are

  • Simple relational knowledge

This knowledge is used to store data systematically and in the form of columns. The only thing to know is they contain relation with each other and they very little chances to make an inference which can be later used in inference engines.

The above table can give answers to 

  • Who plays in rock style?
  • Who plays trumpet in rock style?

2. Inheritable knowledge

This type of knowledge can be passed on other agents without having a need of learning again. If an AI agent learns something from a human, then it can pass it to other agents and they can inherit the same without learning again.

This type of knowledge is generally obtained from associated objects and tries to prescribe a new structure which extracts all or selective attributes from existing objects. This type of knowledge is indulged in the design hierarchies which is found in physical, functional and process domains. So the parent attributes try to inherit the knowledge within the hierarchy to prescribe to the child elements.

3 . Inferential knowledge

It defines the knowledge as a formal logic condition and has a strict rule. The knowledge is extracted from objects by studying the relation between them. If we take a word to make an inference, it will difficult except we take a phrase to get more meaningful insights of that same word. In linguistic, this approach is known as semantics. The new information extracted from the existing information does not require gathering of data from the source but they analyze the existing information in order to generate new knowledge.

4. Procedural knowledge

This knowledge tends to represent control information which uses the knowledge keeps embedded in the knowledge itself. This approach can easily represent heuristic or domain specific knowledge. They are represented as small programs of how to proceed and perform specific things.  They may include inferential efficiency but they do not have inferential adequacy or acquisitional efficiency.

Knowledge representation theory is suitable when intelligent behavior solely depends on explicitly represented knowledge. Knowledge representation is not capable to solve anything by itself if a system fails to reason what it has represented explicitly in the mist effective way. Knowledge representation is a study of the information we can extract in a computationally dependable way or investigating the area within the theories of KR hypothesis. If a theory consumes classical first order logic assumptions, then knowledge representation is the basis of this investigation or else it is recommended to explore other theories.

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Table of contents

Knowledge Representation in AI and Its Business Significance

What is knowledge representation, there are two primary concepts in knowledge representation:, #1 knowledge, #2 representation.

  • 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, 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

what is knowledge representation techniques

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Cycle of knowledge representation in ai.

what is knowledge representation techniques

  • 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

  • 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.
  • 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.
  • 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.
  • 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, 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.

  • 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.

  • 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, frequently asked questions.

  • >> 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.
  • >> 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.
  • >> 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
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2. Inheritable knowledge:

  • In the inheritable knowledge approach, all data must be stored into a hierarchy of classes.
  • All classes should be arranged in a generalized form or a hierarchal manner.
  • In this approach, we apply inheritance property.
  • Elements inherit values from other members of a class.
  • This approach contains inheritable knowledge which shows a relation between instance and class, and it is called instance relation.
  • Every individual frame can represent the collection of attributes and its value.
  • In this approach, objects and values are represented in Boxed nodes.
  • We use Arrows which point from objects to their values.

Knowledge Representation in Artificial intelligence

3. Inferential knowledge:

  • Inferential knowledge approach represents knowledge in the form of formal logics.
  • This approach can be used to derive more facts.
  • It guaranteed correctness.
  • Marcus is a man
  • All men are mortal Then it can represent as; man(Marcus) ∀x = man (x) ----------> mortal (x)s

4. Procedural knowledge:

  • Procedural knowledge approach uses small programs and codes which describes how to do specific things, and how to proceed.
  • In this approach, one important rule is used which is If-Then rule .
  • In this knowledge, we can use various coding languages such as LISP language and Prolog language .
  • We can easily represent heuristic or domain-specific knowledge using this approach.
  • But it is not necessary that we can represent all cases in this approach.

Requirements for knowledge Representation system:

A good knowledge representation system must possess the following properties.

  • 1. Representational Accuracy: KR system should have the ability to represent all kind of required knowledge.
  • 2. Inferential Adequacy: KR system should have ability to manipulate the representational structures to produce new knowledge corresponding to existing structure.
  • 3. Inferential Efficiency: The ability to direct the inferential knowledge mechanism into the most productive directions by storing appropriate guides.
  • 4. Acquisitional efficiency- The ability to acquire the new knowledge easily using automatic methods.

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Book cover

The Knowledge Frontier pp 1–43 Cite as

What Is Knowledge Representation?

  • Nick Cercone 3 &
  • Gordon McCalla 4  

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3 Citations

Part of the book series: Symbolic Computation ((1064))

In this chapter, we overview eight major approaches to knowledge representation: logical representations, semantic networks, procedural representations, logic programming formalisms, frame-based representations, production system architectures, and knowledge representation languages. The fundamentals of each approach are described, and then elaborated upon through illustrative examples chosen from actual systems which employ the approach. Where appropriate, comparisons among the various schemes are drawn. The chapter concludes with a set of general principles which have grown out of the different approaches.

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Gordon McCalla

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Cercone, N., McCalla, G. (1987). What Is Knowledge Representation?. In: Cercone, N., McCalla, G. (eds) The Knowledge Frontier. Symbolic Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4792-0_1

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School of Library and Information Science

Public library association 2024 conference re-cap with katie hansen.

All the PLA Scholarship Winners with Mychal Threets

Working as a remote part-time graduate assistant and a public library circulation clerk makes me often feel like I’ve got my feet in two different spaces. One minute I’m helping young patrons build a foam fort in the DVD section, and the next minute I’m discussing the merits of trauma-informed librarianship with my entire library management class. It’s a weird yet fulfilling duality. 

Thankfully, the Public Library Association (PLA) made it possible for me to meet many more librarians who embrace these and other roles during this year’s 2024 PLA Conference. Through their newly expanded scholarship program, the PLA awarded 151 scholarships to under-represented librarians, part-time librarians, student librarians, and rural librarians, making their attendance possible, (and mine!) I was elated when I received the email confirming my scholarship award, which prompted the realization that I would soon be meeting some of the most integral librarians within today’s public library sphere. Little did I know how significant these three days of learning would soon become to me. 

PLA Panelists

The first day of the conference programming was a flurry of information, introductions, and flagrant fangirling. One of my favorite sessions that day was, Anti-Racist Reader Services: Beyond the Basics with Becky Spratford, Robin Bradford, and Yaika Sabat. If ever there was a rally cry to keep fighting the good fight for representation and well-rounded readers advisory, it was this one-hour session, with librarians sitting wall-to-wall on the carpet floor. 

“Change does not happen until you are uncomfortable, if you don’t feel challenged, you need to go deeper,” said Spratford. 

With a crowd filled with practice-based questions, Bradford, Sabat, and Spratford, gave us each tools, inspiration, and real-world advice on tackling difficult situations to make our libraries a safe and welcoming environment for all. 

Frankly, the sense of coming home is a sentiment folks often share when they’ve found a space that makes them feel welcomed, valued, and supported. While that feeling certainly came to light when I switched my career path from marketing to public librarianship; sitting amongst these rows of librarians and community advocates, I understood that I’d finally found a group, and a profession, where I not only felt welcomed, but encouraged, supported, and celebrated. Not a single person at the conference heard me say “I work in a rural library,” and rolled their eyes, or slowed in conversation, rather, they did the opposite. With the additional context, conversations often became more engaging, with attendees offering additional resources to help me learn about practices and programs they had already implemented. It was during this conference that I got to know the heart that beats within the field of public librarianship, so naturally, I fell in love with the profession all over again.

Molly Knox Ostertag and Katie Hansen at the APA Luncheon

However, the excitement didn’t stop there. During lunch on that first day, I had another core-memory come to life while meeting Molly Knox Ostertag, author, and illustrator, at the Audio Publishing Association luncheon event. For folks who have not yet read the incredible graphic novels by her, I not only highly recommend them, but insist you give them a try if you are so able! As an LGBTQIA+ creator, Knox Ostertag was one of the first authors I turned to upon moving through my own coming out journey. Her debut novel, The Girl by the Sea , is filled with intimate and complex female relationships, mixed with a supportive sapphic narrative, and imagery that will forever and always feel like a warm hug. Getting to listen to her and other authors speak at the luncheon event, meeting Knox Ostertag, getting my books signed, and then receiving custom illustrations inside the books, all made my heart do cartwheels in a way I’d forgotten was possible. All this to say, fangirling, asking for pictures, and sharing Dungeons & Dragons stories all made me feel like being a graphic novel nerd was not only a benefit to the libraries I serve, but personally fulfilling for me as well. This entire luncheon event was a much-needed reminder that serving and taking care of myself is just as important as serving and taking care of my patrons and my community.  

Another personally impactful program from Wednesday’s sessions was Building a Gender-Inclusive Library: Birth Through Early Elementary  with Pearl Bashakevitz and Beckett Czamecki. These two amazing librarians from the Denver Public Library helped me and other participants learn vocabulary and techniques to confront discrimination head-on while supporting gender nonconforming patrons. 

One example, I appreciated was learning not to say, “Boys and girls,” when kicking off a children’s story time, but rather “Okay everyone,” or “Okay friends.” Thankfully, one brave audience member approached the microphone and shared another phrase that they used at her library. 

“Hello critters and creatures!” 

It made my heart sing to hear the room burst with applause and laughter, and to have our lovely presenters insist on using it themselves at their next story time. 

While there are many aspects of myself that can feel undesirable when living in small-town Iowa, at the PLA conference I found a renewed sense of self, and belief in the work I’m doing. As one of my mentors often remind me, “Showing up as yourself gives others permission to do the same.” 

The rest of the conference was filled with making meaningful connections, cheering on our Iowa presenter, and getting to know public library leaders from across the country. On the final day of my trip, I was sad to leave, but also immensely grateful for the countless strangers and new friends who took the time to pour into my cup over these past three days. Driving out of Columbus, I made myself promise that one day, I would be back at PLA, only then, I’d be a MLIS graduate and full-time librarian (perhaps even with a presentation of my own!) Until then, I’ll be putting my new knowledge to use and reading as many graphic novels as I can carry.

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COMMENTS

  1. Knowledge representation and reasoning

    Knowledge representation and reasoning (KRR, KR&R, KR²) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language.Knowledge representation incorporates findings from psychology about how humans solve problems ...

  2. PDF What Is a Knowledge Representation?

    Role 1: A Knowledge Representation Is a Surrogate. Any intelligent entity that wants to reason about its world encounters an important, inescapable fact: Reasoning is a process that goes on internally, but most things it wants to reason about exist only externally. A pro-gram (or person) engaged in planning the assembly of a bicycle, for ...

  3. Knowledge Representation in AI

    Logic-Based Methods: Knowledge is represented using formal logic in logic-based techniques, such as propositional logic, first-order logic, or fuzzy logic. These methods enable exact and rigorous representation, allowing AI systems to carry out logical inference and reasoning. Semantic Networks: Semantic networks are ways to describe knowledge ...

  4. Techniques of Knowledge Representation

    Knowledge representation, which refers to the techniques of capturing and storing human knowledge in a way that machines can understand and use, is a critical component of AI. The main challenge in knowledge representation is finding a way to represent knowledge that is understandable by machines and can be used for reasoning and problem-solving.

  5. Knowledge Representation in AI Explained in Simple Terms

    Knowledge Representation in AI Explained in Simple Terms. Artificial intelligence (AI) is a popular and innovative technology that takes human intelligence to the next level. It offers the power of accurate intelligence integrated with machines. Humans are bestowed with high-level thinking, reasoning, interpreting, and understanding of knowledge.

  6. PDF 1. What Is Knowledge Representation?

    paper is intended to provide some background to knowledge representation research by mapping out the basic approaches to knowledge representation that have developed over the years. It should also serve as an introduction to this collection by providing context for the other articles. In most early AI systems. knowledge representation was

  7. PDF Knowledge Representation

    Outline 1 Representation systems Categories and objects Frames Events and scripts Practical examples - Cyc - Semantic web Philipp Koehn Artificial Intelligence: Knowledge Representation 7 March 2024

  8. Knowledge Representation in AI: The Foundation of Intelligent Systems

    Traditional knowledge representation techniques are often based on static knowledge bases, which can limit the ability of AI systems to adapt and learn from real-time data. By developing techniques that can handle dynamic data, AI systems will be able to continuously update and refine their knowledge, leading to more accurate and up-to-date ...

  9. Knowledge Representation

    Knowledge representation is an active area of research in artificial intelligence (Brachman and Bector 2004).It often refers to the complex and time-consuming technical process performed by knowledge engineers (Knowledge Engineering) when acquiring domain knowledge for use in knowledge-based systems.The question of how to represent human knowledge is an old problem, and knowledge ...

  10. What Is a Knowledge Representation?

    Abstract. Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it—What is it?—has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation ...

  11. PDF Knowledge Representation and Reasoning

    - Knowledge Representation & Reasoning by Brachman & Levesque (available online) • Lectures - Tuesday and Thursday, 12:50-2:05, 300-300 ... - The focus will be on applying representation techniques to real world knowledge and using existing tools to reason with that knowledge - Minor programming may be needed for some assignments.

  12. PDF Chapter 12. Knowledge Representation Techniques in Artificial

    Knowledge Representation Techniques in AI 209 2. Four Main Styles of Knowledge Representation In the present section, an overview is given of four main styles of symbolic knowledge representation used in AI: (a) logic, (b) production rules, (c) pro­ cedures, and (d) semantic networks and frames. Within each style, there are

  13. Knowledge Representation

    Knowledge representation, in this view, involves large, complex structures of symbols, defined and assembled by hand. This approach to AI essentially derives from a line of philosophical thought running from Descartes through Leibnitz, Frege, and Russell. ... Knowledge-based techniques have been applied successfully for many computational tasks ...

  14. What is Knowledge Representation in AI?

    Knowledge Representation in AI describes the representation of knowledge. Basically, it is a study of how the beliefs, intentions, and judgments of an intelligent agent can be expressed suitably for automated reasoning. One of the primary purposes of Knowledge Representation includes modeling intelligent behavior for an agent.

  15. AI Techniques of Knowledge Representation

    Techniques of knowledge representation. There are mainly four ways of knowledge representation which are given as follows: Logical Representation. Semantic Network Representation. Frame Representation. Production Rules. 1. Logical Representation. Logical representation is a language with some concrete rules which deals with propositions and has ...

  16. [PDF] What Is a Knowledge Representation?

    It is argued that keeping in mind all five of these roles that a representation plays provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field. Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? -- has ...

  17. PDF What Is A Knowledge Representation?

    5. Medium of expression and communication. "Possible" vs. reasonably obvious and natural. All five roles matter. The five roles characterize the "spirit" of a representation. The spirit should be indulged, not overcome. "Programming the representation". If it doesn't fit naturally, design a new one.

  18. A Gentle Introduction to Knowledge Representation Learning

    Knowledge representation learning (KRL) mainly focus on the process of learning knowledge graph embeddings, while keeping the semantic similarities. This has proven extremely useful, as feature inputs, for a wide variety of prediction and graph analysis tasks [1, 2, 3]. We will further elaborate on specific applications in future articles.

  19. What Is a Knowledge Representation?

    Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? -- has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties ...

  20. What is Knowledge Representation in AI?

    Knowledge representation is one such process which depends on the logical situation and enable a strategy to take a decision in acquiring knowledge. There are many types and levels of knowledge acquired by human in daily life but machines find difficult to interpret all types of knowledge. For such conditions, knowledge representation is used.

  21. Classifying Knowledge Representation In Artificial Intelligence

    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.

  22. Knowledge Representation in Artificial Intelligence

    Approaches to knowledge representation: There are mainly four approaches to knowledge representation, which are givenbelow: 1. Simple relational knowledge: It is the simplest way of storing facts which uses the relational method, and each fact about a set of the object is set out systematically in columns.

  23. What Is Knowledge Representation?

    Abstract. In this chapter, we overview eight major approaches to knowledge representation: logical representations, semantic networks, procedural representations, logic programming formalisms, frame-based representations, production system architectures, and knowledge representation languages. The fundamentals of each approach are described ...

  24. Public Library Association 2024 Conference Re-Cap with Katie Hansen

    One of my favorite sessions that day was, Anti-Racist Reader Services: Beyond the Basics with Becky Spratford, Robin Bradford, and Yaika Sabat. If ever there was a rally cry to keep fighting the good fight for representation and well-rounded readers advisory, it was this one-hour session, with librarians sitting wall-to-wall on the carpet floor.