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Med Sci Monit. 2002 Jul;8(7):MT124-36.

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.

Author information

1
Centre for Measurement and Information in Medicine, School of Informatics, City University, London, UK. M.Nabih-Kamel-Boulos@city.ac.uk

Abstract

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 Prot g -2000. A metadata collection form helps acquiring resource instance data within Prot g . The DC subject field is populated with UMLS terms directly imported from UMLS Knowledge Source Server using UMLS tab, a Prot g -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 Prot g -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.

PMID:
12118210
[Indexed for MEDLINE]
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