On Homeland Security and the Semantic Web: A Provenance and Trust Aware Inference Framework

Li Ding, Pranam Kolari, Tim Finin, Anupam Joshi, Yun Peng, and Yelena Yesha

Discovering and evaluating interesting patterns and semantic associations in vast amount of information provided by many different sources is an important and time-consuming work for homeland security analysts. By publishing or converting such information in semantic web language, intelligent agents can automate the inference without compromising the semantics. This paper describes how trust and provenance can be represented/obtained in the Semantic Web and then be used to evaluate trustworthiness of discovered semantic associations and to make discovery process effective and efficient.

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