Intelligent Knowledge Organization and Exploration through Topic Modeling and Data Management - 2026_IDR_DEIB_57

The increasing availability of large-scale textual and graph-structured data requires novel approaches for organizing knowledge and supporting its effective exploration. However, classical data management systems often provide limited support for discovering semantic structures, understanding knowledge evolution, and enabling intuitive exploration of complex information spaces. This project aims to investigate innovative methods for intelligent knowledge organization by combining topic modeling, data management, information retrieval, and graph-based representations. The proposed approach will leverage NLP and LLMs to automatically identify, characterize, and track evolving topics, while developing interactive techniques for semantic search, knowledge navigation, and exploratory analysis over heterogeneous knowledge bases. The proposed methods will be validated on large collections of scientific literature, legislative documents, regulatory text, and other knowledge-intensive corpora.