Managing Semantic Content for the Web Amit Sheth, Clemens Bertram, David Avant, Brian Hammond, Krysztof Kochut, and Yashodhan Warke (2002) Voquette and the University of Georgia

Developed by the University of Georgia and Venquette SCORE SCORE := Semantic Content Organization and Retrieval Engine Four key capabilities: Semantic organization and use of metadata Classification with a combination of different methods, e.g. proabilistic (Bayesian), learning (Hidden Markov Models) and knowledge-based Semantic normalization Same metadata for same content Semantic search Semantic association Concept linking (e.g. with rdfs:sameAs) Developed by the University of Georgia and Venquette

SCORE – Components Metadata extraction and classification (both stored in content metabase) Entity extraction (stored in knowledge base) API for semantic application

SCORE – Architecture Diagram 1 2 3

First approach of a semantic content organization and retrieval engine Summary First approach of a semantic content organization and retrieval engine Voquette website offline -> Project not active maintained Not able to find the software for further analysis Architecture could be simplified with current technologies (e.g. the use of a triplestore)