Collaborative Semantic Content Management: an Ongoing Case Study for Imaging Applications
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Data
2010
Autori
Ciuciu, Ioana
Meersman, Robert
Schmid, Jerome
Magnenat-Thalmann, Nadia
Iglesias Guitián, José Antonio
Gobbetti, Enrico
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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 Ontology-Grounded
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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Keywords
knowledge management , content management systems , collaborative ontology engineering , social web , CMS