Implementing a Data Infrastructure for a Pharmaceutical Community

Abstract

Pharmaceutical community has members of various organizations such as from pharmacies, medicines agencies, pharmaceutical companies, and pharmacy oriented educational institutions. All these organizations publish medicinal information in their Web pages. These pages have different kinds of relationships but these relationships are not explicitly presented and therefore searching related pages is frustrating and time consuming. To alleviate this problem we have designed a specific data infrastructure and a vocabulary (OWL-ontology) for linking related pages. Further within each community member these web pages are annotated by linking them into domain specific ontologies. Thus, a community wide integrated ontology is weaved, which can be queried by SPARQL. In this paper, we illustrate how we have exploited the ideas of Linked Data in integrating the domain ontologies of the members of the pharmaceutical community, and how single SPARQL queries can be used in querying the data located in various data sources. In addition the architecture of the developed data infrastructure is presented.

Selective dissemination of clinical guidelines in healthcare communities

“…Data in RDF is presented by threepart statements, which are called triples. Further, they are comprised of subject, predicate, and object [25]. Such triples are also often understood as the thing being described, its property name, and property's value [20].…”

“…Each SPARQL query specifies the RDF-triples (or their pieces), that meets the stated conditions [25]. The conditions resemble RDF triples but the difference is that they may also have variables to add flexibility with respect how they match against the stored RDF-triples.…”

“…SPARQL query language provides two choices for remote queries: these are FROM keyword notation and SERVICE keyword notation [25]. The FROM keyword names a dataset (a local or remote file) to query.…”

“…5, after the variable ?role in the first subquery is bound, the processor takes the same input parameter in calling the second subquery. This kind of feature of accessing two datasets, which is analogous with the join operation of SQL, is very useful as in healthcare communities there are a variety of reasons to cross-reference data between different data sources [25]. Cross-referencing is very useful feature as it gives to user an illusion of integrated files.…”