Knowledge Graphs with Deep Learning Models for Automated Fact Extraction from Unstructured Text

Abstract

Knowledge Graphs with Deep Learning Models for Automated Fact Extraction from Unstructured Text remain a crucial area of research across academia and industry. By bridging the gap between complex textual data and structured representations, these approaches facilitate advanced data understanding, integration, and inference. In this work, we propose a comprehensive framework that leverages knowledge graphs and deep learning for extracting facts from massive unstructured corpora. Our method automatically identifies entities, relationships, and relevant contexts, thereby improving the accuracy and coverage of downstream tasks. Through a combination of advanced neural architectures and robust graph-based inferencing techniques, we aim to systematically demonstrate how multi-modal and domain-agnostic fact extraction can be achieved. Experiments on diverse datasets further validate the scalability and precision of the proposed solution. This paper presents a detailed overview of key principles and theoretical foundations, discusses implementation details, and highlights the evaluation methodology and performance metrics. Our results indicate that the integration of knowledge graphs with deep learning not only achieves competitive benchmarks but also offers interpretability and logical consistency. We conclude by outlining several open challenges and future directions that arise from the complexity and dynamic nature of unstructured text, underscoring the need for continued innovation in this interdisciplinary research domain.