Biomedical Engineering Reference
In-Depth Information
16.3.14 Linked Data queries for translational
research
Finding sources of data with content of interest inside a large research
organisation is often a challenging task. A typical experience for a
scientist involved fi nding out about the data source and associated
applications by word of mouth, looking for the owner of the data in
order to gain access to it, which may require an in-depth discussion of the
terms and conditions of the license agreement, and fi nally installation of
new software tools on their desktop. If a scientist was interested in an
external data source they would need access to IT resources to bring a
copy of the data in-house and potentially provide an interface, as well as
discussing the licensing terms and conditions with the legal department.
These challenges will only become more prevalent as pharmaceutical
companies increasingly embrace external innovation and are confronted
with the need to reconcile many internal and external data sources.
KnowIt plays a central role in an internally developed Linked Data
framework by exposing meta-data related to data sources of interest and by
providing a catalogue of Linked Data services to other applications [19, 20].
To help build these services, we developed a C# library that provides other
developers in the organisation with tools to discover and query the
biomedical semantic web in a unifi ed manner and re-use the discovered data
in a multitude of research applications. Details provided by scientists and
system owners inside the wiki augment catalogue this information such as
the contact information for a data source, ontological concepts that comprise
the data source's universe of discourse, licensing requirements and connection
details, preferably via SPARQL endpoints. The combination of Semantic
MediaWiki and the C# library create a framework of detailed provenance
information from which Linked Data triples are derived. Eventually, paired
with triple stores, and tools for mapping relational databases to RDF, this
framework will allow us to access the mass of information available in our
organisation seamlessly, regardless of location or format.
Two applications were designed to demonstrate this framework. First,
we extracted a high-level, static visual map of relationships between data
sources. Next, we developed a plug-in for Johnson & Johnson's Third
Dimension Explorer, a research tool for visualising and mining large sets
of complex biomedical data [21]. The plug-in utilises metadata in KnowIt
to render an interactive visualisation of the data source landscape. Unlike
the static map, the plug-in performs a clustering of data source attributes,
such as ontological concepts and content, expressed by each data source
in order to group similar data sources together.
Search WWH ::
Custom Search