Collaborative Semantic Content Management: an Ongoing Case Study for Imaging Applications

Abstract: This paper presents a collaborative solution for knowledge management, implemented as a semantic content management system (CMS) with the purpose of knowledge sharing between users with different backgrounds. The CMS is enriched with semantic annotations, enabling content to be categorized, retrieved and published on the Web thanks to the Linked Open Data (LOD) principle which enables the linking of data inside existing resources using a standardized URI mechanism. Annotations are done collaboratively as a social process. Users with different backgrounds express their knowledge using structured natural language. The user knowledge is captured thanks to an ontologic approach and it can be further transformed into RDF(S) classes and properties. Ontologies are at the heart of our CMS and they naturally co-evolve with their communities of use to provide a new way of knowledge sharing inside the network. The ontology is modeled following the so-called DOGMA (Developing OntologyGrounded Methods and Applications) paradigm, grounded in natural language. The approach will be demonstrated on a use case concerning the semantic annotation of anatomical data (e.g. medical images).

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