METHODS FOR BUILDING A KNOWLEDGE BASE USING ARTIFICIAL INTELLIGENCE

Аннотация

Искусственный интеллект (ИИ) добился значительных успехов в различных областях, одним из наиболее перспективных приложений является создание баз знаний и управление ими. Базы знаний имеют решающее значение для управления большими объемами данных, обеспечения быстрого доступа к соответствующей информации и поддержки процессов принятия решений в различных областях. В этой статье рассматриваются методы создания и расширения баз знаний с использованием ИИ, уделяя особое внимание таким методам, как машинное обучение, обработка естественного языка Выпуск журнала №-26 Часть–6_ Май –2025 217 MODERN EDUCATION AND DEVELOPMENT (НЛП) и семантические веб-технологии. Исследуя роль ИИ в автоматизации процессов извлечения данных, представления знаний и рассуждений, мы подчеркиваем, как ИИ может сделать базы знаний более эффективными, точными и адаптируемыми. Кроме того, в статье обсуждаются проблемы разработки баз знаний на основе искусственного интеллекта и будущие перспективы интеллектуальных систем управления знаниями. Ключевые слова. Искусственный интеллект, база знаний, машинное обучение, обработка естественного языка, представление знаний, семантическая сеть, извлечение данных, автоматическое рассуждение, интеллектуальные системы. A knowledge base is a structured repository of information that supports decision-making and problem-solving within an organization or system. Traditionally, knowledge bases were developed manually by domain experts, but with the advent of Artificial Intelligence (AI), the process has become significantly more automated and efficient. AI, particularly through techniques like machine learning (ML) and natural language processing (NLP), has enabled the development of dynamic knowledge bases capable of adapting to new information and improving over time. These intelligent knowledge systems are increasingly used in applications ranging from customer support and healthcare to robotics and autonomous systems. Machine learning, a subset of AI, plays a critical role in the development of knowledge bases by automating the process of extracting, organizing, and updating information. ML models can be trained on vast amounts of unstructured data to identify patterns and relationships between different pieces of information. For instance, in the healthcare sector, ML algorithms can analyze medical literature, patient records, and research papers to extract relevant knowledge, which is then stored in a knowledge base for easy access by healthcare providers. Supervised learning techniques are often used to categorize and label data, while unsupervised learning techniques can identify previously unknown relationships and structures in data. Reinforcement learning, on the other hand, can be used to refine Выпуск журнала №-26 Часть–6_ Май –2025 218 MODERN EDUCATION AND DEVELOPMENT the knowledge base over time by continuously learning from user interactions and feedback. Natural Language Processing (NLP) is another AI-driven method that plays a pivotal role in knowledge base construction. NLP techniques enable machines to understand and process human language, making it possible to extract relevant information from textual data sources such as books, articles, or web pages. By using techniques like named entity recognition (NER), sentiment analysis, and text summarization, NLP can identify key facts, entities, and relationships within documents, which are then added to the knowledge base. Furthermore, NLP aids in knowledge representation, which involves organizing the extracted information in a way that makes it accessible and usable. Technologies such as ontologies and knowledge graphs help represent the relationships between different entities in a semantic and structured manner, enabling more intuitive query answering and reasoning over the data. The Semantic Web is a framework for creating intelligent systems that can understand and process data in a meaningful way. By using ontologies—formal representations of knowledge within a specific domain—AI can enhance the structure and interconnectivity of knowledge bases. Ontologies provide a shared vocabulary and define the relationships between various concepts, making it easier to model complex information and ensure consistency across the knowledge base. In addition to ontologies, AI-driven knowledge bases leverage technologies such as Resource Description Framework (RDF) and SPARQL (a query language for databases) to represent and query data in a way that supports inference and reasoning. This allows systems to not only retrieve factual information but also deduce new knowledge from existing data. AI enables automated reasoning and inference processes, which are essential for enhancing the functionality of knowledge bases. Automated reasoning involves applying logical rules to the data stored in the knowledge base to draw conclusions, make predictions, or suggest actions. This capability is particularly useful in systems Выпуск журнала №-26 Часть–6_ Май –20