Biomedical Engineering Reference
In-Depth Information
rapid advances in both information and communication technologies are cre-
ating a new revolution in scientifi c discovery and learning applications. The
focus of these new approaches is effective handling of data, specifi cally the
ability to manage orders-of-magnitude more data than ever before, the ability
to provide these data directly and immediately to a global community, the
ability to use algorithmic approaches to extract meaning from massive
volumes of data, and the ability to seamlessly collect community contributions
and put these to use. For example, Wikipedia has created an entirely new
model by capturing enormous volumes of data and making them freely avail-
able in useful ways, transforming how people fi nd and make use of informa-
tion on a daily basis. Similar but more semantically supported technologies
can help us in an era of e-science where data can be made available to
everyone, not only to professional scientists but also to students, patients, and
teachers. Beyond new scientifi c discoveries, we are at the dawn of a revolu-
tion in collective learning due to these Web-based information and commu-
nication technologies. New applications will give users a way to explore and
understand a vast and rapidly changing world of interoperable scientifi c data.
Increasing by small increments in complexity will make users feel comfortable
so that they can effortlessly see the benefi t of these applications; thus, they
will gain the necessary insight into the processes required to make future
data interoperable. In particular, the interactive and Web-based annotation
tools will be valuable learning aids. These systems will promote familiarity
with the underlying formalisms and technologies necessary for enabling the
Semantic Web. Life science scientists will become capable of spotting subtle
differences in the semantics of seemingly similar concepts in their fi elds.
Although ConceptWiki and associated applications specifi cally target the bio-
logical and chemical domain, the software and methods are intended to be
reusable for any science moving from heterogeneous data to a shared, global,
collaboratory system.
To visualize and identify concepts and assertions found in data repositories,
we have developed a recognition system that provides a graphical interface
for displaying the result from running the text through an indexing system.
The knowledge enhancer is an in-text semantic support application that
exploits the data contained in ConceptWiki to recognize concepts on the fl y
in any website text. Recognized concepts are highlighted with colors specifi c
to the different semantic types. A variety of different functionalities can be
invoked when a highlighted concept-denoting term in the text is clicked. For
example, each unambiguous term detected by the knowledge enhancer is
directly linked to the concept it denotes in ConceptWiki, and therefore all
information accessible in ConceptWiki can be displayed in a popup or in the
left-hand panel frame of the browser. Another function available in the knowl-
edge enhancer popup constructs a query with a concept and all its synonyms,
which are then submitted to multiple search engines. The results are more
comprehensive than only using one possible synonym of the concept. The
knowledge enhancer application may also be developed as a browser plugin
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