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OGK 2011 : 2011 Fall Symposium on Open Government Knowledge: AI Opportunities and Challenges

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Link: http://tw.rpi.edu/ogk2011
 
When Nov 4, 2011 - Nov 6, 2011
Where Arlington, Va, USA
Submission Deadline Jun 3, 2011
Categories    artificial intelligence
 

Call For Papers

Call For Papers

AAAI 2011 Fall Symposium on Open Government Knowledge: AI
Opportunities and Challenges

4-6 November 2011 - Arlington, Virginia USA

http://tw.rpi.edu/ogk2011

The AAAI 2011 Fall Symposium on Open Government Knowledge: AI
Opportunities and Challenges (OGK2011) seeks papers on all
aspects of publishing public government data as reusable
knowledge on the Web. Both long papers presenting research
results and shorter papers describing late breaking work,
outlining implemented systems, identifying new research
challenges, or articulating a position are invited.
Submissions are due by June 3, notifications will be sent by
July 15, and the final camera-ready copy must be provided by
September 9, 2011.

BACKGROUND

Websites like data.gov, research.gov and USASpending.gov aim to
improve government transparency, increase accountability, and
encourage public participation by publishing public government
data online. Although industry and academia have used these for
some intriguing applications, the data in its present form is
hard for citizens to understand and use. Research and
deployment challenges emerging from open government data
practices include the following.

* Scalability. How can we search, access and reuse the
hundreds of thousands of datasets from data.gov as well the
much larger number of datasets directly available at
federal agencies' website? Is there an organic way to
dramatically increase the amount of open government data in
a distributed and collaborative fashion?

* Interoperability. Multi-scale open government data came from
city governments, state governments, and national
governments. How can one compare the GDP of the US and
China, and later link to state-level financial data? Open
government data covers many domains. How can one associate
open government data with domain knowledge to build, e.g. a
cancer prevention application?

* Provenance and quality. How should provenance be leveraged
to facilitate high-quality data management interactions
(e.g. reuse, mash-up and feedback) and community
participation between the government and the public?

* Citizen Involvement. How can linked data application sites
encourage more citizen participation for comments and
contributions, and then how can these more diverse
contributions be tracked, managed, validated, and evaluated?

Several approaches have been proposed to address these
challenges. Using semantic technologies, especially Linked
Data, to enrich the value of such data and ultimately convey
the data to the citizens is one possibility. For example,
linking together Justices' backgrounds, and related supreme
court decisions has the potential to provide a better
understanding of the working of the Supreme Court. Linked Open
Government Data are enabled by Semantic Web technologies such
as RDF, RDFS, SPARQL and RDFa.

Once linked, the value of government data can be greatly
increased with a potential reduction of cost (i) applications
are no longer limited to one or several datasets but can use
all the inter-connected datasets (including non-government
data) on the Web; (ii) data-as-interface allow data curators,
visualizers and analysts incrementally work on a specific
smaller part of data processing independently, (iii) linked
data enables transparent data mining and generates detailed
provenance traces that allow the study of trust, privacy and
policy issues. Using crowd-sourcing to distribute the task of
building parsers and visualizers for different data.gov
datasets is another possibility. Machine learning to find and
explore relationships between data is also a possible approach.

Secondly, for governments to be able to release high quality
datasets, they must be able to express usage access and
restriction policies. To achieve this, provenance mechanisms
must be provided to keep track of which datasets have been used
and how these have been combined and policy mechanisms must be
used to ensure compliance with appropriate usage
restrictions. This involves several interesting areas of
research: machine understandable usage restrictions, provenance
tracking and maintenance, and scalable reasoners capable of
verifying policy compliance.

Lastly, the techniques developed for extracting semantics,
using, and sharing open government datasets can also be applied
to closed/secure datasets for applications such as sharing
private information within/across agencies, and integrating
electronic health records across healthcare organizations. In
this symposium, we invite input from diverse communities
including but not limited to: government data publishers,
developers, user communities who run real systems and generate
demand for new technologies, and the AI community who can
provide solutions and advance the research in the areas
specified above. The location of symposium is extremely
attractive since a lot of open government data practitioners
are conveniently located in Washington, DC.

SUGGESTED TOPICS

* Automatic and semi-automatic creation of linked data
resources
* General ontologies for open linked government data
* Entity linking and co-reference detection between linked
data resources
* Adding temporal qualifications to government data
* Creating mash-ups with open government data
* Scalable solutions for linking open government data
* Linked open government data analysis
* Semantic technologies for government data and applications
* Representing and propagating provenance metadata
* Policies for information sharing, use, and privacy
* Managing usage restrictions and privacy of government data
* Metadata for certainty and trust in linked open government
data
* Social networks in government data
* Publishing results of machine learning applied to open
government data
* Visualization of open government data revealing underlying
patterns and relations

SYMPOSIUM STRUCTURE

This single track symposium will run from 9:00am Friday
November 4 until 12:30pm Sunday November 6 and include a
mixture of invited talks, paper presentations, panels, system
demonstrations, a poster session, and discussions. We plan to
have several invited speakers, e.g., a US federal Government
representative addressing the current status of the US open
government initiative, a researcher discussing open challenges
and a W3C staff member describing the role of current and
future standards in government knowledge. We will also have a
panel to address the emerging issue of health informatics, the
potential nationwide health information network, where private
health data and public governmental data are interconnected. We
are also interested in running a half-day tutorial/hack-a-thon
to provide attendees hands-on experiences in creating Linked
Open Government Data and building mashups. Submissions

We invite submissions of full papers (up to eight pages)
presenting research results and short papers (up to four pages)
defining a position, articulating a new problem or describing a
working system. Papers must be prepared in AAAI format and
submitted using the ogk2011 easychair site. All accepted papers
will be published in a proceedings issued as a AAAI technical
report. Papers should be original material that has not been
previously published or under review for another venue. Late
breaking ideas are encouraged as the subject of a short papers.

IMPORTANT DATES

* 3 June 2011 Submit papers using the ogk2011 site
* 15 July 2011 Notifications sent to authors
* 9 Sept 2011 Camera ready papers due
* 16 Sept 2011 author registration deadline
* 14 Oct 2011 Open pre-registration deadline
* 3 Nov 2011 AI Funding seminar
* 4-6 Nov 2011 Fall Symposium

General symposium information

General information on the 2011 AAAI Fall Symposia will be
available from the 2011 AAAI FSS Website. This includes
information about deadlines, registration, location,
transportation, and hotel accommodations. Organizers

* Li Ding, Rensselaer Polytechnic Institute
* Tim Finin, University of Maryland, Baltimore County
* Lalana Kagal, MIT
* Deborah McGuinness, Rensselaer Polytechnic Institute

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