Researchers have formulated an analytical framework designed to measure and compare evolving topic dynamics across heterogeneous artificial intelligence document streams at multiple levels of conceptual abstraction. The rapid growth of artificial intelligence involves diverse communities, including academic scientists pursuing theoretical breakthroughs, engineers developing software models, and practitioners discussing commercial deployments. These distinct groups record their progress in divergent textual formats, such as scientific preprints, open-source repositories, and industry newsletters, which differ substantially in vocabulary, technical granularity, and temporal patterns. Traditional scientometric tools struggle to synthesize these fragmented archives because they predominantly evaluate single sources or rely on flat topic classifications that fail to capture multi-level structural evolution or community differences. Published in EPJ Data Science, the new methodology addresses these limitations by mapping textual documents from disparate sources into shared hierarchical taxonomies, establishing a rigorous foundation for multi-resolution comparative analysis without imposing restrictive single-source constraints or inventing ad-hoc labeling categories.