Generative AI for Automated Knowledge Extraction from Large-Scale Data

Abstract

Generative Artificial Intelligence has become a revolutionary paradigm in extraction of automated knowledge in heterogeneous data sources of large scale. More complex traditional extraction systems tend to be rule-based or supervised, meaning that they need much annotation by hand and domain knowledge. Comparatively, large language models and multimodal transformers have generative AI models that can extract knowledge and reason semantically and structure unstructured knowledge contextually. The presented study explores how generative AI will be used to automate the process of knowledge extraction in the fields of healthcare, cybersecurity, scientific research, and smart cities. The study suggests a complex procedure supporting generative embedding models, semantic inference pipelines, and building up of the knowledge graph. As shown in experiments, generative AI considerably enhances accuracy of extraction, context awareness, and scalability to the conventional machine learning methods. The findings also show that unstructured text, images and multimodal data can be analyzed effectively using the generative AI, and automated discovery of knowledge is made possible. Nevertheless, the presence of, among other issues, prejudice, illusion, credibility, and computability cost issues are very important constraints. The paper provides a systematic structure and empirical research on the implementation of generative AI in knowledge mining systems. The results of the study have practical implications in that the findings will be relevant to organizations that are interested in turning large volumes of raw data into AI-driven actionable knowledge assets created in intelligent and scalable systems of AI automation.