STAN: Social, Trusted Annotation Network

Authors:
Hyun Namgoong;Kyoung-Mo Yang;Sung-Kwon Yang;Charles Borchert;Hong-Gee Kim
Affiliations:
Biomedical Knowledge Engineering Lab, Seoul National University, Seoul, Republic of Korea;Department of Computer Science Frank.Holzwarth, SunMoon University, Asan, Republic of Korea;Biomedical Knowledge Engineering Lab, Seoul National University, Seoul, Republic of Korea;Biomedical Knowledge Engineering Lab, Seoul National University, Seoul, Republic of Korea;Biomedical Knowledge Engineering Lab, Seoul National University, Seoul, Republic of Korea
Venue:
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
Year:
2008

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Abstract

Annotated data play an important role in enhancing the usability of information resources. Single users can be easily frustrated by the task of annotating. Collaborative approaches to annotation have been applied to web resources, but have not yet been applied to the task of local documents, due in part to the lack of a uniform identification method. In this paper, we use hash-based virtual URIs for identifying documents, and introduce the concept of a STAN (Social, Trusted Annotation Network), which enables collaborative annotation of documents through their URIs. STAN also incorporates quantitative trust rates between users in social networks based on their interactions with each other. The STAN framework is described, demonstrating how these trust networks are constructed through collaborative annotation. Finally, we evaluate the usefulness of collaborative annotation and the feasibility of the resulting trust rates through empirical experiment.