FIAT/IFTA World Conference 2026

We present an initial methodological framework for representing events in the automated cataloguing of journalistic audiovisual content. Integrated into a broader research project on the automation of document analysis in newsrooms, this study examines how eventbased metadata can be effectively modelled using controlled vocabularies, without requiring structural modifications to existing MAM indexing
systems. The approach operationalizes the 5W model—adapted here as 4W + 1H—through lightweight, nonontological solutions suitable for legacy documentation environments.


The research draws on a corpus of more than 5,000 news items related to the 2024 DANA weather event in the Valencian Community. Using a representative sample, the study analyses three types of documentary elements—documents, segments, and stories—to identify common metadata patterns, assess their potential for reuse in cascading automated workflows, and determine the structural limits of storylevel aggregation in news production contexts.


The results reveal persistent issues such as overindexing, multisemantic fields, weak traceability, and inconsistent responses to the 5W framework. The study examines whether advanced semanticweb structures (ontologies, knowledge graphs) are necessary, or whether enhancing existing controlled vocabularies and refining metadata field architectures can provide sufficient robustness and operational value.


The findings underscore the need to improve metadata structures whose semantics depend both on their formal design and on their functional placement within the document record, as key factors for enhancing retrieval, consistency, and automation in audiovisual news cataloguing.