Metadata Extraction: Challenges and Approaches

This paper delves into the complexities involved in automatically extracting metadata from scientific documents, particularly focusing on the limitations of existing metadata extraction methods. It introduces a hybrid approach that combines machine learning techniques with traditional rule-based systems to improve the accuracy and efficiency of metadata extraction. The study emphasizes the critical role of metadata in organizing, retrieving, and understanding scientific knowledge. By proposing a more effective extraction methodology, the paper aims to enhance the efficiency of research workflows, supporting the growing need for precise and accessible metadata in academic research and data-driven discovery.