Key research themes
1. How can document structure modeling and ontology-based data organization reduce ambiguity and improve data management in digital documents?
This theme addresses methods for representing and managing the structure and semantics of digital documents to ensure data is stored and retrieved unambiguously. It tackles issues arising from traditional relational database models that often lose context due to normalization and lack of redundancy. By adopting ontology-oriented data management and advanced metamodels, this research area aims to enable compact, context-rich, and precise digital document aggregation, which is vital for efficient information processing and knowledge management.
2. What are effective computational methods for automatic document structure analysis and transformation to support information retrieval and reuse?
This theme focuses on algorithmic and representational techniques for automatic detection, recognition, and transformation of document structures—such as paragraphs, tables, and semantic zones—especially in formats like PDF or scanned images. It includes methodologies to convert unstructured or semi-structured digital documents into machine-readable, semantically enriched, and ontologically represented content, facilitating improved retrieval, reuse, and cross-application interoperability.
3. How can user interactions and end-user behavior impact document quality and processing efficiency in natural language digital documents?
This research area studies the effects of end-user activities on the quality and resource costs of text-based digital documents, emphasizing the role of user proficiency in computational thinking and document handling. It investigates logging of user actions during document creation and editing to quantify the extra effort incurred by malformatted or erroneous documents, thereby proposing methodologies for pretesting, error detection, and improved user guidance to reduce inefficiencies and financial losses.





