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considerations (e.g. the set of features must be
decided arbitrarily), which are inherently highly
biased by the aspects being put into examination.
However, we plan for the future to offer an overall
view of the features offered by most notable
annotation systems at the current state of the art, and
observe if these can be enabled in our framework.
By emphasizing the amount of development effort
necessary when developing a system with specific
features, and the effort that is required to master our
framework and build those same features over it, we
can obtain a fair map of the improvements and
benefits in adopting it. Regarding further evolutions,
while the framework seems to us general enough in
its basic assumptions, we want to improve it in terms
of concrete support to developers. We will thus
increment the set of available conventions and create
template libraries for recurring annotation patterns.
These libraries will provide partial implementations,
which can be bound to specific needs through
dedicated extension points. Our interest in semi-
supervised processes for knowledge acquisition
(Fiorelli et al., 2010) motivates our attention to
integrating automatic extraction engines and to
combining them with proper human interaction, into
more virtuous acquisition workflows. We have
already explored this approach in (Pazienza et al.,
2012), with the development of a text analytics
system for the discovery of new semantic relations
among concepts belonging to the AGROVOC
thesaurus (Caracciolo et al., 2012). We plan to
integrate this system to the proposed framework and,
in the meanwhile, extend its scope to the projection
of arbitrary information onto an ontology.
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