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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 169 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. 6. REFERENCES [1] O’Reilly, T. Government as a Platform. Innovations, vol 6, no.1, pp 13-40, 2010. [2] DiMaio, A., Government 2.0: A Gartner Definition, 2009. http://blogs.gartner.com/andrea_dimaio/2009/11/13/governm ent-2-0-a-gartner-definition/, last retrieved 28 February 2012. [3] Bonson, E., Torres, L., Royo, S., and Flores, F., Local e- Government 2.0: Social Media and Corporate Transparency in Municipalities, Government Information Quarterly, vol. 29, pp 123-132, 2012. [4] Government of Singapore, Government Social Media Directory, available at: http://www.socialmedia.gov.sg/Web/ Home/Default.aspx, last retrieved 15 April 2012. [5] Bertot, J.C, Jaeger, P.T, and Hansen, D., The Impact of Polices on Government Social Media Usage: Issues, Challenges, and Recommendations, Government Information Quarterly, vol. 29, pp 30-40, 2012. [6] Lorenzetti, C., Maguitman, A. A Semi-supervised Incremental Algorithm to Automatically Formulate Topical Queries. Information Science. Elsevier. 179 (12), 2009. [7] Simari, G., Rahwan, I. (eds), Argumentation in Artificial Intelligence, Springer Verlag, 2009. [8] Estévez, E., Chesñevar, C., Maguitman, A., Brena, R. DECIDE 2.0 – A Framework for Intelligent Processing of Citizens’ Opinion in Social Media. In Proc. dg.o 2012, Maryland, USA, pp.266-267, ACM Press, 2012. [9] Guo, L., Lease, M. Personalizing Local Search with Twitter. SIGIR 2011 Workshop on Enriching Information Retrieval (ENIR 2011).July 24–28, 2011, Beijing, China. Available at: http://select.cs.cmu.edu/meetings/enir2011/papers/guolease.pdf. [10] Maguitman, A., Leake, D., Reichherzer, T., Suggesting novel but related topics: towards context-based support for knowledge model extension, Proc. of the 10th IUI Conf., San Diego, California, USA, January 2005. [11] Twitter Sentiment Corpus, available at: http://www. sananalytics.com/lab/twitter-sentiment/, retrieved April 2012. [12] Bing Liu. "Sentiment Analysis: A Multifaceted Problem." IEEE Intelligent Systems, 25(3), pp. 76-80, 2010. [13] Maguitman, A., Leake, D., Reichherzer, T., Menczer, F. Dynamic Extraction of Topic Descriptors and Discriminators: Towards Automatic Context-Based Topic Search. Proceedings of the 13 th CIKM Conf.. ACM Press. Washington, DC, USA, November 2004 [14] García, A., Simari, G. Defeasible Logic Programming: An Argumentative Approach. Theory and Practice of Logic Programming 4(1-2): 95-138, 2004. [15] Chesñevar, C., Maguitman, A, Simari, G. Recommender Systems based on Argumentation, in "Emerging Artificial Intelligence Applications in Computer Engineering". Maglogiannis et al (eds). Frontiers in Artificial Intelligence and Applications, Vol. 160, pp. 53-70.. IOS Press, 2007.
[16] Galitsky, B., McKenna, E. Sentiment Extraction from Consumer Reviews for Providing Product Recommendations. US Patent Application US 2009/0282019 A1 170
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2012 ALBANY, NEW YORK UNITED STATES
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Foreword The 6 th International Con
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Table of Contents Forward .........
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Session 10: ICTs & Governance Trans
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o Revival of Farming Community with
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International Steering Committee Jo
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Jim DAVIES, Computing Laboratory, U
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Session 1 Open Government Data
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the combination of their moral sent
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involve the creation of a Network o
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approach [28] to expose indicators
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4. IMPLENTING PAR In order to come
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conclusions that might arise rom th
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6.3 Large Scale Implementation In r
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Implementation Framework for Open D
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2) Usage: Assessed by quantifying t
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Increasing Kenyan Open Data Consump
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insight into available data resourc
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New Perspectives for Electronic Gov
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Not without reason therefore that t
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Another principle adopted by this s
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access to public information, chang
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Open Government 2.0: Citizen Empowe
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As a third finding, we also found a
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Session 2 E-democracy & Participati
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two polarized (optimistic or utopia
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through aggregating, shaping, and c
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in-depth expert interviews, and wer
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government made of citizens’ cont
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[8] Putman, R.D. 1994. Making Democ
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The third stream of studies reviewe
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interests, gaining access to inform
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2. THEORETICAL FOUNDATION 2.1 The P
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epresentatives from the three main
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While letters to the editor could b
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person. Table 5: Public sphere char
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[32] Yang, S., H. Lee, and S. Kurni
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In March 2000, just a couple of mon
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society the gap will close [2]. The
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A New Roadmap for Next-Generation P
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needs and the activities of policy-
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[18] Moss, S., (2010). Policy Model
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2.1. National digital inclusion exp
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advanced initiatives specifically d
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involved later [22]. Several instit
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among lecturers and students parall
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2. COMPARATIVE E-GOVERNMENT IN LATI
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governments can innovate or be disc
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2. E-GOVERNMENT AS IS 2.1. E-Govern
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ankings on the formation of nationa
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Framework for Useful Transparency W
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are doing a good job overall in reg
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The Circular Continuum of Agencies,
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continue to rely on dial-up Interne
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this time the authors continue to w
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government agencies should consider
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The critical success factors for we
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include customers and competitors,
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Websites or columns for MFWs In thi
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According to our investigation, the
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Construction of Inter-regional Publ
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The Development of Public Smart Pho
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Stage Description 1 st stage (Simpl
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Understanding Citizens’ Perceptio
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performance compared to those provi
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Session 5 Information Sharing, Mode
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arising as a result of these two bi
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forms for the system. At the same t
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further investigation of the relati
Page 145 and 146: 5.3.10 Commitment Both personal and
Page 147 and 148: law. Virginia Journal of Internatio
Page 149 and 150: available to others” [5]. Informa
Page 151 and 152: conflict around information sharing
Page 153 and 154: for understanding information polic
Page 155 and 156: primarily on the information flows
Page 157 and 158: On Public Service Provision Informa
Page 159 and 160: employs workflow management technol
Page 161 and 162: o Information. Represents any infor
Page 163 and 164: common documents repository at the
Page 165 and 166: Figure 15. Full model of Passport I
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Page 169 and 170: policies to prevent government agen
Page 171 and 172: ecause the majority of the shared i
Page 174 and 175: A Patent System Ontology for Facili
Page 176 and 177: uilt on top of the semantics of RDF
Page 178 and 179: dynamically derive relevancy based
Page 180 and 181: Figure 6. Cross-‐Reference betw
Page 182 and 183: Category II: After identifying some
Page 184 and 185: [8] De Nicola, A., Missikoff, M. an
Page 186 and 187: oral option, it also allows for a m
Page 188 and 189: mapping of citizens would ensure un
Page 190 and 191: Here the authors wish to extend the
Page 192 and 193: http://www.ecp.gov.pk/Reports/SPStr
Page 194 and 195: will enable to assess and confront
Page 198 and 199: Preparing for Digital Curation Gove
Page 200 and 201: semester hours. Nine of the forty-f
Page 202 and 203: REGNET: Regulatory Information Mana
Page 204 and 205: Search Terms/ Concepts References D
Page 206 and 207: 4. COMPARATIVE STUDIES OF REGULATOR
Page 208 and 209: 1105.4 [6] Content of Section 1105.
Page 210 and 211: Pollution Prevention Guide for Vehi
Page 213 and 214: From Information-Poor to Informatio
Page 215 and 216: To address the divide, specific gov
Page 217 and 218: ICT for Development and the MuNet P
Page 219 and 220: competencies and skills, contributi
Page 221 and 222: ICT Policy in Africa - A Comparativ
Page 223 and 224: essential and necessary to guide th
Page 225 and 226: Kenya it is the minister for Inform
Page 227 and 228: of existing Public Internet Access
Page 229 and 230: the policy process e.g. formulation
Page 231 and 232: Laggards or Victims of Socioeconomi
Page 233 and 234: Figure 3. Facilities & Services Enj
Page 235 and 236: Promoting user uptake of e-governme
Page 237 and 238: It was also decided that it was nec
Page 239: Session 8 Measurement 1
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6. ACKNOWLEDGEMENT We are grateful
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aspects are hardly discussed as the
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service level agreements. The inter
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concensus of all participants, the
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Furthermore, it is understood from
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Mobile Technologies on the Internet
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of mothers. Based on this reason, w
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5.1.4 Precedence Plots Based on com
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3 indicators are similar but not th
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Latitude 10°�S 9°�S 8°�S 7
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7. CONCLUSION According to ORDIT an
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include but not limited to: payment
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Categories Only XSS (%) Table 2: Di
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From table 3, it can be deduced tha
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Session 9 Social Media in Governmen
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1. What kind of social media servic
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4.2.1 Facebook Usage Figure 3. Perc
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Government Crisis Communication on
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estoration theory. He termed it as
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Table 3. Strategies for Pre-crisis
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accounts, among which seven message
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information in the crisis event sta
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Government Official Microblogs: An
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It is worth mentioning that, while
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Managing Chinese Government Microbl
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due to their tremendous influence,
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3. ACKNOWLEDGEMENT This research is
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3) Established year: to specify the
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Figure 6. Language used in Hong Kon
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E-Government and Transformation of
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and domestic as well as internation
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4.6 Computerisation of Railway Tick
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abysmally low (2.25). In addition t
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Evaluating and Assessing a Typology
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Exhaustiveness indicates that all c
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Fig. 1 Spatial Unit Fig. 3. By whom
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SUPPLEMENTARY TABLE 1. RESULT OF SE
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Explaining the eGovernment Paradox:
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eGovernment services to a large ext
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policy agenda, as Heeks [10] says,
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Implementation Plan’s outcomes an
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Conference on eGovernment (pp. 90-9
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Investigating the Relationships bet
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hybrid or authoritarian regime. Bov
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Technologies, Tools and Web 2.0 in
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with resources, peers and experts w
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cluster were: Court Clerks; Court,
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(8 questions) and the third subpart
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http://ux.brookdalecc.edu/governanc
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the examination of the 311 program
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3. CASE STUDY METHOD This study use
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discuss departmental performance at
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or laptop computers. Internet conne
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[2] Anavitarte, L. and Tratz-Ryan,
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A Literature Review: IT Governance
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Interviewees Useful? Table 3. Inter
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Assessment of Success Factors of e-
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In view of different theoretical ap
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approximate figure of 60% of intern
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difference between its priority and
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Computer Supported Contractor Selec
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(2) conduction of the public auctio
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E-Government Success Factors in the
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creating different incentives and p
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American and the Caribbean Institut
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* Note: A 7-points Likert scale was
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showed to be useful, for collecting
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Public CIO, Figurehead or Decision-
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4.2 Operative CIO The Operative CIO
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Session 12 Implementation & Impact
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NYSED is responsible for collecting
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considered markers that are able to
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many of the MDGs. The direct impact
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strongly believed that local govern
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Goal Variables Measurements 2 3 Com
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Hypothesis Variables Endogenous Exo
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[16] Joreskog, K.G., and Sorbom, D.
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GoN has addressed the challenge to
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Figures 6 and 7. Computer classes a
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ICT-enabled Delivery of Maternal He
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3. E-HEALTH 3.1 ICTs and Health Com
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Chart 1 [40] GHANA - Total Telephon
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change their behaviour, improve the
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activities such as the MoTeCH partn
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If We Build it Will They Come? A Mi
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The methodology contains inherent l
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information about their broadband s
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Session 13 E-democracy & Participat
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attempt to influence future behavio
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Evidence shows that cognitive facto
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success factors for eParticipation[
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structure of the forum as well as t
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government as well as augmenting di
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Figure 1: Message content 4.3 Messa
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Figure 6: Associated levels 4.10 At
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improve the government management e
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Session 14 Information Structure
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2. HEALTHCARE IN RURAL INDIA Health
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Cloud Data Center Internet Service
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Reliability of data: data collected
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Information System (NHIS). A Nation
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charge are either not aware of the
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from this medium. Additional securi
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attacks require the adversary to ma
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actions with yet unknown recipients
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Session 15 Measurement 2
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2. LITERATURE REVIEW 2.1 Electronic
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Among the main limitations and chal
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Furthermore, our study demonstrates
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E-Government Portals in Central Ame
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13 Guadaloupe 31.74 14.19 14 Aruba
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Evolving e-Government Benchmarking
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judgments are made about the qualit
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percentage of online availability o
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methodology, and few national gover
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integrating them worth it? Because
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Impacts of the Public Procurement R
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Not all the municipalities have the
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3. HYPHOTESIS AND RELEVANT QUESTION
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58% of the buyers believe that the
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A strategy of evolution of the mark
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Posters
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Russian Federation legislative prop
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Figure 1. Document Flow and E-DRM P
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We further assume that each key tar
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egion that is more representative o
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2.2 C-DAC C-DAC’s PARAM series of
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The Metropolitan Area Planning Coun
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expanded functionality in three are
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For each case, semi-structured inte
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Figure 1. Comparative analysis on t
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projects and their on-site images.
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key challenges of Enterprise System
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5. PART THREE: E GOVERNMENT APPLICA
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Following the research objectives,
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From Inter-Agency Information Shari
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479
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more and more valuable with the gro
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Implementation of Integrated Tax Sy
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Information Sharing in Inter-depart
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Interagency Collaboration in Provid
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489
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noted. The list of basic e-services
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the description of procedure for mo
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Opportunities for Improving eGovern
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Prioritization and Geo-Spatiotempor
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[4] R.Bruggemann and Ganapati P. Pa
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equirements. A direct communication
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Stage - 2: Here the actual software
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membership associations, recreation
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Fig 1 : Agriculture Revolution 3. P
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sector, private sector, and civil s
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System For Measuring e-Gov: A Multi
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The Digital Divide’s Devaluing of
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[3] Coursey, D., & Norris, D.F. (20
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In Brazil, electronic accessibility
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2) The areas to which mobile and wi
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2. OVERVIEW OF DISTRICT MAPPING The
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3.4 Driving License Driving License
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and socio-economic backgrounds. The
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Figure - 2: eGovernment Implementat
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Tutorial: Digital Curation for Publ
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ut also provide sufficient detail o
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ABM is known as a bottom-up simulat
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5.2 Relation to Empirics “Where d
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Larson, Elizabeth .................
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