Biomedical Engineering Reference
In-Depth Information
in a standard format. This standardization of formatting and nomenclature
is the critical fi rst step toward an integrated, computable network of
biomedically relevant data. It also provides the foundation for the
formatting and integration of further data sets over time.
19.4 The design and features of TripleMap
Based on experience working with Big Data and in the biomedical
research and development sector, we set out over three years ago to
design and develop a next-generation semantic search and analysis
system, which we named TripleMap. Users are welcome to register and
login to the free web-based version of TripleMap at
www.triplemap.com
in order to test out its various features. The public instance of TripleMap
is completely functional, lacking only the administrator features found in
Enterprise deployments within an organization. Below, we outline
TripleMap's features and then provide additional details regarding three
aspects of the system, the Generated Entity Master (GEM) Semantic Data
Core, the TripleMap semantic search interface, and collaboration through
knowledge map creation and sharing.
TripleMap has a number of features that distinguish it from more
traditional enterprise text-based search systems. In particular, it enables
collaborative knowledge sharing and makes it possible for organizations
to easily build an instance behind their fi rewall for their internal data.
These features are outlined here.
■
Text search equivalent to the current best of breed systems: searching in
the TripleMap system is comprehensive including the entire contents of
all documents in the system. It also provides relevancy scoring in a
manner similar to text search engines such as Google, FAST, and Endeca.
■
Next-generation semantic search: as a next-generation semantic search
platform, TripleMap goes beyond the capabilities of text search engines
by giving users the ability to search not only for text in documents but
also for entities, their associations, and their meta-data.
■
Display and navigation of entity to entity associations: upon fi nding a
given entity, the user is able to easily identify other entities which are
associated with their original input. Furthermore, the user is able to
navigate through associations and identify novel associations as they
move through information space.
■
Automated extraction of entity properties, labels, and associations:
TripleMap is able to automatically derive the properties, labels, and
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