Affiliations: [a] City University of New York, College of Staten Island,
Staten Island, NJ, USA. E-mail: Soon.Chun@csi.cuny.edu | [b] Georgian Court University, Lakewood, NJ, USA. E-mail:
warnerj@georgian.edu | City University of New York, College of Staten Island | University of Massachusetts, Amherst | Universidad Autónoma del Estado de México | University of Southern California – Information
Science Institute
Abstract: Collaboration and information sharing among government organizations
is becoming increasingly important for promoting efficiency and productivity as
well as for enhancing citizen services. With Internet connectivity widely
available and with the ease-of-use of social media tools, citizens actively
participate in producing content. However, the abundance of content causes
another problem for governments – the difficulty of determining what truly
useful and relevant information is to be shared for mission critical tasks and
to produce better citizen services. Information resources, such as data,
documents, multimedia objects and services stored in different agencies and
produced by citizens need to be easily discovered and shared. We propose a
data model of rich social tags and a Citizen-Government
collaborative tagging environment} where governments and citizens can
collaboratively annotate the resources, thus facilitating collaboration
responsiveness through accessibility to information. The collaborative
annotations capture not only the semantics but also the pragmatic and social
aspects related to the resources, such as who, when, where, how and for what
related tasks the resources are shared. The benefits of the a rich tag data
model emphasizing the relationships of a tag to semantic, social, pragmatic and
contextual reference frames include the ability to filter, discover and search
new and dynamic as well as hidden resources, to navigate between resources in a
search by traversing semantic relationships, and to recommend the most relevant
government information even when distributed over different agencies. A
distributed architecture of a government collaborative tagging system is
proposed and tag-based search and recommendations are illustrated.
Keywords: Semantic annotation, collaborative tagging, social tagging, pragmatic tagging, tag pivoting
