Investigations into Trust for Collaborative Information Repositories: A Wikipedia Case Study
|Investigations into Trust for Collaborative Information Repositories: A Wikipedia Case Study|
|Author(s)||McGuinness, Deborah L., Honglei Zeng, Paulo Pinheiro da Silva, Li Ding, Dhyanesh Narayanan, Mayukh Bhaowal|
|Published in||Proceedings of the Workshop on Models of Trust for the Web|
|Keyword(s)||Trust, Wikipedia, Inference Web, Proof Markup Language, Open Editing.|
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Investigations into Trust for Collaborative Information Repositories: A Wikipedia Case Study is a 2006 conference paper by McGuinness, Deborah L., Honglei Zeng, Paulo Pinheiro da Silva, Li Ding, Dhyanesh Narayanan, Mayukh Bhaowal and published in Proceedings of the Workshop on Models of Trust for the Web.
As collaborative repositories grow in popularity and use, issues concerning the quality and trustworthiness of information grow. Some current popular repositories contain contributions from a wide variety of users, many of which will be unknown to a potential end user. Additionally the content may change rapidly and information that was previously contributed by a known user may be updated by an unknown user. End users are now faced with more challenges as they evaluate how much they may want to rely on information that was generated and updated in this manner. A trust management layer has become an important requirement for the continued growth and acceptance of collaboratively developed and maintained information resources. In this paper, we will describe our initial investigations into designing and implementing an extensible trust management layer for collaborative and/or aggregated repositories of information. We leverage our work on the Inference Web explanation infrastructure and exploit and expand the Proof Markup Language to handle a simple notion of trust. Our work is designed to support representation, computation, and visualization of trust information. We have grounded our work in the setting of Wikipedia. In this paper, we present our vision, expose motivations, relate work to date on trust representation, and present a trust computation algorithm with experimental results. We also discuss some issues encountered in our work that we found interesting.
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