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 The DRESS framework constitutes the central focus of the PhD research at the UPV, conducted under the supervision of the SID Group at the University of Zaragoza.
DRESS is a framework designed to automatically enrich and enhance private, isolated digital libraries.
Its core purpose is to transform siloed data repositories into intelligent, user-centric knowledge systems by integrating internal data with external semantic web resources.
The key characteristics of the framework are:
- Automated Enrichment: Leverages Natural Language Processing (NLP) and Information Extraction to identify, classify, and link entities within the library's content.
- Knowledge Base Population: Builds a comprehensive knowledge base by connecting internal data with external sources like public knowledge bases (e.g., DBpedia, Wikidata).
- Enhanced User Experience: Improves discovery and interaction through features like semantic search, infoboxes, and personalized content recommendations.
- Ontology-Driven: Uses a domain ontology to guide the extraction processes, ensure data consistency, and make the system adaptable to various contexts.
- Task Automation: Assists users with complex tasks such as document summarization, report generation, and content personalization.
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