icegov2012 proceedings

The underlying DeLP process is as follows. Given a particular
query Q - e.g. assessing whether ObamaCare Programme is
supported by citizens’ opinions – isSupported(ObamaCare),
DeLP will automatically perform a backward chaining process to
obtain a sequence of defeasible rules instantiations allowing to
conclude Q. Such a sequence is called an argument supporting Q.
Clearly, as contradictory information can be present in the
defeasible knowledgebase, counter-arguments might arise (e.g. an
argument supporting ~isSupported(ObamaCare). When two
arguments A and B are in conflict, a preference criterion is used in
DeLP to determine which argument should prevail. The argument
that prevails will be called a defeater. The preference criterion to
be applied (e.g. specificity [14]) can be defined in a modular way.
Note that the notion of defeat among arguments may lead to
complex “cascade” situations: an argument A may be defeated by
an argument B, which in turn may be defeated by an argument C,
and so on. Besides, every argument involved may have on its turn
more than one defeater. Argumentation systems allow us to
determine when a given argument is considered as ultimately
acceptable with respect to the knowledge we have available by
means of a dialectical analysis, which takes the form of a tree-like
structure called dialectical tree. The root of the tree is a given
argument A supporting some claim, and children nodes for the
root are those defeaters B1, B2, .. Bk for A. The process is repeated
recursively on every defeater Bi, until all possible arguments have
been considered. The tree leaves are arguments without defeaters.
Some additional restrictions apply - e.g. the same argument
cannot be used twice in a path, as that would be fallacious and
would lead to infinite paths.
In DECIDE 2.0 we expect to have a knowledge engineer with
expertise in DeLP, who will be in charge (along with egovernment
officials) of characterizing a knowledge base of strict
and defeasible rules for assessing valuable aspects for policymaking
oriented decisions. Through suitable visualization tools,
the DeLP machinery will provide an automated dialectical
analysis of arguments and counterarguments when facing a
problematic situation (associated with a particular topic or context
for decision making), helping government officials to make better
and more informed decisions. It must be remarked that DeLPbased
recommender systems have been already successfully
formalized [15]. A particular deployment of DeLP for integrating
sentiment analysis and recommendations has also been applied
previously in a commercial software product for travelling
recommendations [16]
4. BRIEF ROADMAP: CONCLUSIONS
Research on the proposed framework will lead directly to
improved coverage, scalability and context-awareness with
respect to the current model of information delivery and retrieval
in social networks. Governments can greatly benefit from the
proposed solution in twofold - by having adequate mining
techniques to retrieve valuable information provided by citizens
on social media, and by targeting different announcements to the
appropriate group of government stakeholders.
In order to materialize the proposed solution, some important
technical problems are to be solved, and constitute research
questions of this work. Major challenges that we intend to address
defining a roadmap for our future work include: 1) developing
algorithms for integrating information - several users may post
messages related to the same topic; hence accrual of information
needs to be modeled properly; 2) implementing efficient models
of trust and reputation propagation - users post information on
social media whose reliability has to be assessed in order to
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effectively use such information for decision making; and 3)
developing customized information models providing targeted
information to various categories of stakeholders requires having
different “views” of the issues under analysis.
5. ACKNOWLEDGMENTS
The research is funded by LACCIR (Latinamerican and Caribbean
Collaborative ICT Research), Microsoft Research, CONACyT
(Mexico) and Interamerican Development Bank (IDB) and PIP
CONICET Projects 112-200801-02798 and 112-200901-00863.
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