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Knowledge Representation in AI

Knowledge Representation in AI is the method of structuring and organizing knowledge so that AI systems can process and utilize it for reasoning and decision-making.

This article aims to provide a comprehensive overview of knowledge representation in AI, exploring its methods, types, techniques, challenges, and applications.

Table of Content

What is Knowledge Representation in AI?

Relationship between knowledge and intelligence, cycle of knowledge representation in artificial intelligence, types of knowledge in ai, approaches to knowledge representation in ai, 1. logical representation, 2. semantic networks, 4. production rules, 5. ontologies, key techniques in knowledge representation, challenges in knowledge representation, applications of knowledge representation in ai.

Knowledge Representation in AI refers to the way in which artificial intelligence systems store, organize, and utilize knowledge to solve complex problems. It is a crucial aspect of AI, enabling machines to mimic human understanding and reasoning. Knowledge representation involves the creation of data structures and models that can efficiently capture information about the world, making it accessible and usable by AI algorithms for decision-making, inference, and learning.

• Knowledge as a Foundation : Knowledge provides the necessary information, facts, and skills that intelligence uses to solve problems and make decisions.
• Intelligence as Application : Intelligence is the ability to learn, reason, and adapt, using knowledge to perform tasks and solve complex problems.
• Interdependence : Knowledge without intelligence is static, while intelligence without knowledge lacks the raw material to function effectively.
• Synergy : Effective AI systems require a balance of both knowledge (the "what") and intelligence (the "how") to operate successfully.

The AI Knowledge Cycle is an ongoing process where AI systems continually acquire, process, utilize, and refine knowledge to enhance performance.

It consists of these key stages:

• Knowledge Acquisition : Gathering data and information from various sources, including databases, sensors, and human input.
• Knowledge Representation : Organizing and structuring this knowledge using techniques like ontologies and semantic networks for effective processing.
• Knowledge Utilization : Applying the structured knowledge to perform tasks, make decisions, and solve problems through reasoning and inference.
• Knowledge Learning : Continuously updating the knowledge base by learning from new data and outcomes using machine learning algorithms.
• Knowledge Validation and Verification : Ensuring the accuracy, consistency, and reliability of the knowledge through validation against real-world outcomes.
• Knowledge Maintenance : Regularly updating the knowledge base to stay relevant and accurate as the environment or information changes.
• Knowledge Sharing : Distributing the knowledge to other systems or users, making it accessible and usable beyond the original AI system.

This cycle repeats itself, with each stage feeding into the next, allowing AI systems to continually improve and adapt.

1. Declarative Knowledge

• Declarative knowledge refers to facts and information that describe the world, answering the "what" type of questions.
• Example : Knowing that Paris is the capital of France.
• This knowledge is often stored in databases or knowledge bases and expressed in logical statements, forming the foundation for more complex reasoning and problem-solving in AI systems.

2. Procedural Knowledge

• Procedural knowledge is the knowledge of how to perform tasks or processes, answering the "how" type of questions.
• Example : Steps to solve a mathematical problem or the procedure to start a car.
• This knowledge is embedded in algorithms or control structures, enabling AI systems to execute tasks, perform actions, and solve problems step-by-step.

3. Meta-Knowledge

• Meta-knowledge is knowledge about knowledge, understanding which types of knowledge to apply in different situations.
• Example : Knowing when to use a specific algorithm based on the problem at hand.
• Crucial for systems that need to adapt or optimize their performance, meta-knowledge helps in selecting the most appropriate strategy or knowledge base for a given problem.

4. Heuristic Knowledge

• Heuristic knowledge includes rules of thumb, educated guesses, and intuitive judgments derived from experience.
• Example : Using an educated guess to approximate a solution when time is limited.
• Often used in problem-solving and decision-making processes where exact solutions are not feasible, helping AI systems to arrive at good-enough solutions quickly.

5. Structural Knowledge

• Structural knowledge refers to the understanding of how different pieces of knowledge are organized and related to each other.
• Example : Understanding the hierarchy of concepts in a taxonomy or the relationships between different entities in a semantic network.
• This knowledge is essential for organizing information within AI systems, allowing for efficient retrieval, reasoning, and inferencing based on the relationships and structures defined.

Logical representation involves using formal logic systems like propositional and predicate logic to represent knowledge in a structured, precise, and unambiguous way.

Logical representation allows AI systems to perform reasoning by applying rules of inference to derive conclusions from known facts. It is commonly used in applications that require rigorous and consistent decision-making, such as theorem proving and rule-based systems.

A semantic network is a graphical representation of knowledge where nodes represent concepts, and edges represent relationships between those concepts.

Semantic networks are used to model hierarchical relationships (like class hierarchies in object-oriented programming) and associative relationships (such as synonymy in natural language processing). They help AI systems understand the connections between different concepts and perform tasks like inference, classification, and ontology mapping.

Frames are data structures that encapsulate knowledge about objects, situations, or events in a structured format. Each frame contains attributes (slots) and their associated values, which can include default values, constraints, and even procedural knowledge.

Frames are used to represent stereotypical situations or objects, allowing AI systems to make inferences based on the structure and relationships within the frames. For example, a frame for a "car" might include slots for make, model, color, and owner, along with rules for filling in missing information.

Production rules are "if-then" statements that express knowledge in the form of conditions and corresponding actions. They are a key component of rule-based systems.

Production rules are used in expert systems, where they form the basis for decision-making and problem-solving. When the condition (if-part) of a rule is met, the corresponding action (then-part) is executed, enabling the AI system to derive conclusions, perform tasks, or generate responses.

An ontology is a formal representation of a set of concepts within a domain and the relationships between them. Ontologies provide a shared vocabulary and a common understanding of a domain, which can be used by both humans and AI systems.

Ontologies are widely used in knowledge management, semantic web technologies, and natural language processing. They enable AI systems to understand the context of information, perform reasoning across different domains, and facilitate interoperability between systems. For example, an ontology for the medical domain might define relationships between diseases, symptoms, and treatments, helping AI systems to diagnose illnesses or suggest treatment options.

1. First-Order Logic (FOL)

First-Order Logic is a formal system used in mathematics, philosophy, and computer science to represent and reason about propositions involving objects, their properties, and their relationships. Unlike propositional logic, FOL allows the use of quantifiers (like "forall" and "exists") to express more complex statements.

FOL is widely used in AI for knowledge representation and reasoning because it allows for expressing general rules and facts about the world. For example, FOL can be used to represent statements like "All humans are mortal" and "Socrates is a human," enabling AI systems to infer that "Socrates is mortal." It provides a powerful and flexible framework for representing structured knowledge and supports various forms of logical reasoning.

2. Fuzzy Logic

Fuzzy Logic is an approach to knowledge representation that deals with reasoning that is approximate rather than exact. It allows for the representation of concepts that are not black and white, but rather fall along a continuum, with degrees of truth ranging from 0 to 1.

Fuzzy Logic is particularly useful in domains where precise information is unavailable or impractical, such as control systems, decision-making, and natural language processing. For example, in a climate control system, fuzzy logic can be used to represent concepts like "warm," "hot," or "cold," and make decisions based on the degree to which these conditions are met, rather than relying on strict numerical thresholds.

3. Description Logics

Description Logics are a family of formal knowledge representation languages used to describe and reason about the concepts and relationships within a domain. They are more expressive than propositional logic but less complex than full first-order logic, making them well-suited for representing structured knowledge.

Description Logics form the foundation of ontologies used in the Semantic Web and are key to building knowledge-based systems that require classification, consistency checking, and inferencing. For example, they can be used to define and categorize different types of products in an e-commerce system, allowing for automated reasoning about product features, relationships, and hierarchies.

4. Semantic Web Technologies

Semantic Web Technologies refer to a set of standards and tools designed to enable machines to understand and interpret data on the web in a meaningful way. Key technologies include Resource Description Framework (RDF), Web Ontology Language (OWL), and SPARQL, which are used to represent, query, and reason about knowledge on the web.

These technologies are essential for building intelligent applications that can access, share, and integrate data across different domains and systems. For example, Semantic Web Technologies are used in search engines, recommendation systems, and data integration platforms to provide more relevant and accurate results by understanding the context and meaning of the data. They enable AI systems to perform tasks like semantic search, data linking, and automated reasoning over distributed knowledge bases.

While knowledge representation is fundamental to AI, it comes with several challenges:

• Complexity : Representing all possible knowledge about a domain can be highly complex, requiring sophisticated methods to manage and process this information efficiently.
• Ambiguity and Vagueness : Human language and concepts are often ambiguous or vague, making it difficult to create precise representations.
• Scalability : As the amount of knowledge grows, AI systems must scale accordingly, which can be challenging both in terms of storage and processing power.
• Knowledge Acquisition : Gathering and encoding knowledge into a machine-readable format is a significant hurdle, particularly in dynamic or specialized domains.
• Reasoning and Inference : AI systems must not only store knowledge but also use it to infer new information, make decisions, and solve problems. This requires sophisticated reasoning algorithms that can operate efficiently over large knowledge bases.

Knowledge representation is applied across various domains in AI, enabling systems to perform tasks that require human-like understanding and reasoning. Some notable applications include:

• Expert Systems : These systems use knowledge representation to provide advice or make decisions in specific domains, such as medical diagnosis or financial planning.
• Natural Language Processing (NLP) : Knowledge representation is used to understand and generate human language, enabling applications like chatbots, translation systems, and sentiment analysis.
• Robotics : Robots use knowledge representation to navigate, interact with environments, and perform tasks autonomously.
• Semantic Web : The Semantic Web relies on ontologies and other knowledge representation techniques to enable machines to understand and process web content meaningfully.
• Cognitive Computing : Systems like IBM's Watson use knowledge representation to process vast amounts of information, reason about it, and provide insights in fields like healthcare and research.

Knowledge representation is a foundational element of AI, enabling machines to understand, reason, and act on the information they process. By leveraging various representation techniques, AI systems can tackle complex tasks that require human-like intelligence. However, challenges such as complexity, ambiguity, and scalability remain critical areas of ongoing research. As AI continues to evolve, advancements in knowledge representation will play a pivotal role in the development of more intelligent and capable systems.

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Knowledge Representation in AI

Humans are great at tasks that require creativity , critical thinking , and empathy . They can learn from experience and adapt to new situations, and they possess emotional intelligence that allows them to understand and connect with other people on a deep level.

On the other hand, Artificial Intelligence or AI is excellent at tasks that require speed, accuracy, and scalability. It can quickly process vast amounts of data and perform complex calculations and analyses far beyond human capabilities.

But how does AI understand knowledge or data for its benefits? This article will give you the perfect answer to this question.

Introduction

Knowledge representation is a crucial element of Artificial Intelligence. It is believed that an intelligent system needs to have an explicit representation of its knowledge to reason and make decisions.

Knowledge representation provides a framework for representing, organizing, and manipulating knowledge that can be used to solve complex problems, make decisions, and learn from data.

For example, when you see a hot tea cup, a signal immediately comes from your brain cautioning you against picking it up. If we were to make AI more sophisticated(or humanist), we would be required to feed them with more and often complex information about our world to perform the complex task, which leads to the concept of Knowledge Representation in Artificial Intelligence.

What is Knowledge Representation in AI?

Knowledge representation is a fundamental concept in artificial intelligence (AI) that involves creating models and structures to represent information and knowledge in a way that intelligent systems can use. The goal of knowledge representation is to enable machines to reason about the world like humans, by capturing and encoding knowledge in a format that can be easily processed and utilized by AI systems.

There are various approaches to knowledge representation in AI, including:

Logical representation: This involves representing knowledge in a symbolic logic or rule-based system , which uses formal languages to express and infer new knowledge.

Semantic networks: This involves representing knowledge through nodes and links, where nodes represent concepts or objects, and links represent their relationships .

Frames: This approach involves representing knowledge in the form of structures called frames , which capture the properties and attributes of objects or concepts and the relationships between them.

Ontologies: This involves representing knowledge in the form of a formal, explicit specification of the concepts, properties, and relationships between them within a particular domain.

Neural networks: This involves representing knowledge in the form of patterns or connections between nodes in a network, which can be used to learn and infer new knowledge from data.

The Different Kinds of Knowledge: What to Represent

• Object: The AI needs to know all the facts about the objects in our world domain. E.g., A keyboard has keys, a guitar has strings, etc.
• Events: The actions which occur in our world are called events.
• Performance: It describes a behavior involving knowledge about how to do things.
• Meta-knowledge: The knowledge about what we know is called meta-knowledge.
• Facts: The things in the real world that are known and proven true.
• Knowledge Base: A knowledge base in artificial intelligence aims to capture human expert knowledge to support decision-making, problem-solving , and more.

Types of Knowledge in AI

In AI, various types of knowledge` are used for different purposes. Here are some of the main types of knowledge in AI:

Declarative Knowledge: This knowledge can be expressed in a declarative form, such as facts, rules, or propositions. It is also called descriptive knowledge and is expressed in declarative sentences. It is often represented using logic-based representations such as knowledge graphs or ontologies. Example: The capital of France is Paris. This statement represents declarative knowledge because it is a fact that can be explicitly stated and written down. It is not based on personal experience or practical skills, but rather on an established piece of information that can be easily communicated to others.

Procedural Knowledge: This knowledge is used to perform specific tasks or actions and is often represented using algorithms or programming languages . It is responsible for knowing how to do something. It includes rules, strategies, procedures, agendas, etc. Example: How to change a flat tire on a car, including the steps of loosening the lug nuts, jacking up the car, removing the tire, and replacing it with a spare. This is a practical skill that involves specific techniques and steps that must be followed to successfully change a tire.

Meta-knowledge: This is knowledge about knowledge and is often used to reason about and improve the performance of AI systems. Example: To remember new information, it is helpful to use strategies such as repetition, visualization, and elaboration. This statement represents metaknowledge because it is knowledge about how to learn and remember new information, rather than knowledge about a specific fact or concept. It acknowledges that some specific techniques and strategies can be used to enhance memory and learning, and encourages the use of these techniques to improve learning outcomes.

Heuristic Knowledge: Heuristics are based on past experiences or domain knowledge and are often used in decision-making processes to guide an AI system toward a solution. Heuristic knowledge is a type of knowledge in AI that refers to rules of thumb or strategies that are used to solve problems quickly and efficiently, but only sometimes optimally. Heuristics are often used when there is too much complexity or uncertainty in a problem to use an exact algorithm or solution. Example: When packing for a trip, it is helpful to make a list of essential items, pack versatile clothing items that can be mixed and matched, and leave room in the suitcase for any souvenirs or purchases. This statement represents heuristic knowledge because it is a practical set of rules of thumb that can be used to guide decision-making in a specific situation (packing for a trip).

Structural Knowledge: This is knowledge about the structure of a problem or system and is often used to help AI systems decompose complex problems into simpler sub-problems that can be solved more easily. It is the basic knowledge of problem-solving. It also describes relationships between concepts such as kind of, part of, and grouping of something. Example: In the field of biology, living organisms can be classified into different taxonomic groups based on shared characteristics. These taxonomic groups include domains, kingdoms, phyla, classes, orders, families, genera, and species. This statement represents structural knowledge because it describes the hierarchical structure of the taxonomic classification system used in biology. It acknowledges that there are specific levels of organization within this system and that each level has its unique characteristics and relationships to other levels.

The Relation Between Knowledge and Intelligence

Knowledge and intelligence are related but distinct concepts. Knowledge refers to the information, skills, and understanding that an individual has acquired through learning and experience. In contrast, intelligence refers to the ability to think abstractly, reason, learn quickly, solve problems, and adapt to new situations.

In the context of AI, knowledge, and intelligence are also distinct but interrelated concepts. AI systems can be designed to acquire knowledge through machine learning or expert systems. Still, the ability to reason, learn, and adapt to new situations requires a more general intelligence that is beyond most AI systems' capabilities.

An agent can only act accurately on some input when it has some knowledge or experience about that input.

Nonetheless, using knowledge-based systems and other AI techniques can help enhance the intelligence of machines and enable them to perform a wide range of tasks.

AI Knowledge Cycle

The AI knowledge cycle is a process that involves the acquisition, representation, and utilization of knowledge by AI systems. It consists of several stages, including:

Data collection: This stage involves gathering relevant data from various sources such as sensors, databases, or the internet.

Data preprocessing: The collected data is then cleaned, filtered, and transformed into a suitable format for analysis.

Knowledge representation: This stage involves encoding the data into a format that an AI system can use. This can include symbolic representations, such as knowledge graphs or ontologies, or numerical representations, such as feature vectors.

Knowledge inference: Once the data has been represented, an AI system can use this knowledge to make predictions or decisions. This involves applying machine learning algorithms or other inference techniques to the data.

Knowledge evaluation: This stage involves evaluating the accuracy and effectiveness of the knowledge that has been inferred. This can involve testing the AI system on known examples or other evaluation metrics.

Knowledge refinement: Based on the evaluation results, the knowledge representation and inference algorithms can be refined or updated to improve the accuracy and effectiveness of the AI system.

Knowledge utilization: Finally, the knowledge acquired and inferred can be used to perform various tasks, such as natural language processing , image recognition , or decision-making .

The AI knowledge cycle is a continuous process, as new data is constantly being generated, and the AI system can learn and adapt based on this new information. By following this cycle, AI systems can continuously improve their performance and perform a wide range of tasks more effectively.

Approaches to Knowledge Representation

Simple relational knowledge.

• This type of knowledge uses relational methods to store facts.
• It is one of the simplest types of knowledge representation.
• The facts are systematically set out in terms of rows and columns.
• This type of knowledge representation is used in database systems where the relationship between different entities is represented.
• There is a low opportunity for inference.

Inheritable Knowledge

• Inheritable knowledge in AI refers to knowledge acquired by an AI system through learning and can be transferred or inherited by other AI systems.
• This knowledge can include models, rules, or other forms of knowledge that an AI system learns through training or experience.
• In this approach, all data must be stored in a hierarchy of classes.
• Boxed nodes are used to represent objects and their values.
• We use Arrows that point from objects to their values.
• Rather than starting from scratch , an AI system can inherit knowledge from other systems, allowing it to learn faster and avoid repeating mistakes that have already been made. Inheritable knowledge also allows for knowledge transfer across domains, allowing an AI system to apply knowledge learned in one domain to another.

Inferential Knowledge

• Inferential knowledge refers to the ability to draw logical conclusions or make predictions based on available data or information
• In artificial intelligence , inferential knowledge is often used in machine learning algorithms, where models are trained on large amounts of data and then used to make predictions or decisions about new data.
• For example, in image recognition, a machine learning model can be trained on a large dataset of labeled images and then used to predict the contents of new images that it has never seen before. The model can draw inferences based on the patterns it has learned from the training data.
• It represents knowledge in the form of formal logic.

Example: Statement 1: Alex is a footballer. Statement 2: All footballers are athletes. Then it can be represented as; Footballer(Alex) ∀x = Footballer (x) ———-> Athelete (x)s

Procedural Knowledge:

• In artificial intelligence , procedural knowledge refers to the knowledge or instructions required to perform a specific task or solve a problem.
• This knowledge is often represented in algorithms or rules dictating how a machine processes data or performs tasks.
• For example, in natural language processing, procedural knowledge might involve the steps required to analyze and understand the meaning of a sentence. This could include tasks such as identifying the parts of speech in the sentence, identifying relationships between different words, and determining the overall structure and meaning of the sentence.
• One of the most important rules used is the If-then rule.
• This knowledge allows us to use various coding languages such as LISP and Prolog .
• Procedural knowledge is an important aspect of artificial intelligence, as it allows machines to perform complex tasks and make decisions based on specific instructions.

Requirements For Knowledge Representation System

Representational accuracy.

Representational accuracy refers to the degree to which a knowledge representation system accurately captures and reflects the real-world concepts, relationships, and constraints it intends to represent. In artificial intelligence, representational accuracy is important because it directly affects the ability of a system to reason and make decisions based on the knowledge stored within it.

A knowledge representation system that accurately reflects the real-world concepts and relationships that it is intended to represent is more likely to produce accurate results and make correct predictions. Conversely, a system that inaccurately represents these concepts and relationships is more likely to produce errors and incorrect predictions.

Inferential Adequacy:

Inferential adequacy refers to the ability of a knowledge representation system or artificial intelligence model to make accurate inferences and predictions based on the knowledge that is represented within it. In other words, an inferentially adequate system can reason and draw logical conclusions based on its available information.

Achieving inferential adequacy requires a knowledge representation system or AI model to be designed with a well-defined reasoning mechanism that can use the knowledge stored within it. In addition, this mechanism should be able to apply rules and principles to the available data to make accurate inferences and predictions .

Inferential Efficiency

Inferential efficiency in artificial intelligence refers to the ability of a knowledge representation system or AI model to perform reasoning and inference operations in a timely and efficient manner. In other words, an inferentially efficient system should be able to make accurate predictions and draw logical conclusions quickly and with minimal computational resources .

Achieving inferential efficiency requires several factors, including the complexity of the reasoning mechanism, the amount and structure of the data that needs to be processed, and the computational resources available to the system. As a result, AI researchers and developers often employ various techniques and strategies to improve inferential efficiency, including optimizing the algorithms used for inference, improving the data processing pipeline, and utilizing specialized hardware or software architectures designed for efficient inferencing.

Acquisitional efficiency

Acquisitional efficiency in artificial intelligence refers to the ability of a knowledge representation system or AI model to effectively and efficiently acquire new knowledge or information. In other words, an acquisitionally efficient system should be able to rapidly and accurately learn from new data or experience.

Achieving acquisitional efficiency requires several factors, including the ability to recognize patterns and relationships in the data, the ability to generalize from examples to new situations, and the ability to adapt to changing circumstances or contexts. AI researchers and developers often employ various techniques and strategies to improve acquisitional efficiency, including active learning, transfer learning, and reinforcement learning.

The key takeaways from this article are:-

• Knowledge representation is a fundamental concept in artificial intelligence (AI) that involves creating models and structures to represent information and knowledge in a way that intelligent systems can use.
• Objects, events, performance, meta-knowledge , facts, and knowledge-base are the different kinds of knowledge.
• The AI knowledge cycle is a process that involves the acquisition, representation, and utilization of knowledge by AI systems.
• Relational, inferential, procedural , and inheritable are four approaches to knowledge representation.

Q. How is AI used in cybersecurity?

A. AI (Artificial Intelligence) is increasingly used in cybersecurity to improve the efficiency and effectiveness of various security measures. Here are some ways in which AI is used in cybersecurity:

Threat detection: AI algorithms can be trained to identify patterns and anomalies in network traffic, which can help detect potential threats and attacks. These algorithms can monitor network activity, log files, and other data sources to identify unusual behavior and respond to potential threats.

Malware detection: AI can identify and classify different types of malware. AI-powered antivirus software can use machine learning algorithms to learn from past malware behavior and detect new variants.

Fraud detection: AI can detect fraudulent activity in financial transactions, such as credit card fraud or money laundering. AI algorithms can analyze large amounts of data and identify patterns that may indicate fraudulent activity.

Vulnerability assessment: AI can scan systems and networks for vulnerabilities that attackers could exploit. AI-powered vulnerability scanners can analyze system configurations and identify potential security weaknesses.

Incident response: AI can automate incident response processes, such as isolating infected systems, blocking malicious traffic, and restoring compromised data.

User authentication: AI can be used to analyze user behavior patterns to detect anomalies and prevent unauthorized access. For example, AI-powered systems can learn how users typically access a system and identify if a user's unusual behavior indicates a potential security threat.

Q. Will AI take over cybersecurity?

A. No, AI will not take over cybersecurity entirely. While AI can potentially improve the efficiency and effectiveness of various security measures, it is not a substitute for human expertise in cybersecurity.

AI can help automate routine tasks such as malware detection, but it still requires human oversight and intervention to ensure the accuracy of the results. Moreover, AI is not infallible and can make mistakes or be vulnerable to attacks. Therefore, human cybersecurity experts are still needed to evaluate and interpret the results generated by AI-powered systems and to make decisions based on their expertise and experience.

Q. What is AI in cybersecurity?

A. In cybersecurity, AI (Artificial Intelligence) refers to using machine learning algorithms and other AI techniques to enhance various security measures. AI-powered cybersecurity systems can analyze large amounts of data, detect patterns, and make decisions based on that analysis without requiring human intervention .

Structured Methods of Representation of the Knowledge

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This paper describes same of the structured methods of representation of the knowledge that we introduced as an alternative to the procedures of representation more formal. After a brief mention to the essential characteristics that we have to take into account to any structured method of representation of the knowledge, classified into declarative methods and procedural methods. Declarative methods give more importance to the facts and entities of the domain that to the mechanisms of manipulation of the same. By contrary, the procedural methods, although they operate on facts and entities of the domain of speech, gives greater attention to the mechanisms of relation between entities. The declarative methods studied in this paper are the semantic network in which the knowledge is represented like a collection of joined nodes among them by means of labeled arches, the frames that can be defined as complex semantic network that treat the problem of the representation from the optics of the reasoning for likeness. Moreover, we described the formalism of the production rules, putting special attention in their structure and their form of cooperating with some declarative of representation (for example frames).

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Sara Rodríguez

Javier Prieto

GECAD - Instituto Superior de Engenharia, Porto, Portugal

Pedro Faria

Department of Computer Science and Production Management, University of Zielona Góra, Zielona Góra, Poland

Sławomir Kłos

Computing Science and Artificial Intelligence, Rey Juan Carlos University, Móstoles, Madrid, Spain

Alberto Fernández

Basque Center for Applied Mathematics, Bilbao, Spain

Santiago Mazuelas

Basque Center for Applied Mathematics, Universidad de Alcalá, Alcalá de Henares, Spain

M. Dolores Jiménez-López

Departamento de Informática y Automática, University of Salamanca, Salamanca, Spain

María N. Moreno

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Elena M. Navarro

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Pinto, F.J. (2019). Structured Methods of Representation of the Knowledge. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_24

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