Scimago Lab
powered by Scopus
call: +1.631.470.9640
Mon-Fri 10 am - 2 pm EST


Medical Science Monitor Basic Research


eISSN: 1643-3750

Get your full text copy in PDF

Towards a semantic medical Web: HealthCyberMap’s tool for building an RDF metadata base of health information resources based on the Qualified Dublin Core Metadata Set

Maged N. Kamel Boulos, Abdul V. Roudsari, Ewart R. Carson

Med Sci Monit 2002; 8(7): MT124-126

ID: 510682

Background:     HealthCyberMap (http://healthcybermap.semanticweb.org/) aims at mapping Internet health information resources in novel ways for enhanced retrieval and navigation. This is achieved by collecting appropriate resource metadata in an unambiguous form that preserves semantics.
Material/Methods:     We modelled a qualified Dublin Core (DC) metadata set ontology with extra elements for resource quality and geographical provenance in Protégé-2000. A metadata collection form helps acquiring resource instance data within Protégé. The DC subject field is populated with UMLS terms directly imported from UMLS Knowledge Source Server using UMLS tab, a Protégé-2000 plug-in. The project is saved in RDFS/RDF.
Results:     The ontology and associated form serve as a free tool for building and maintaining an RDF medical resource metadata base. The UMLS tab enables browsing and searching for concepts that best describe a resource, and importing them to DC subject fields. The resultant metadata base can be used with a search and inference engine, and have textual and/or visual navigation interface(s) applied to it, to ultimately build a medical Semantic Web portal. Different ways of exploiting Protégé-2000 RDF output are discussed.
Conclusions:     By making the context and semantics of resources, not merely their raw text and formatting, amenable to computer 'understanding,' we can build a Semantic Web that is more useful to humans than the current Web. This requires proper use of metadata and ontologies. Clinical codes can reliably describe the subjects of medical resources, establish the semantic relationships (as defined by underlying coding scheme) between related resources, and automate their topical categorisation.

This paper has been published under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) allowing to download articles and share them with others as long as they credit the authors and the publisher, but without permission to change them in any way or use them commercially.
I agree