Description: 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 SemanticWeb and then be used to evaluate trustworthiness of discovered semantic associations and to make discovery process effective and efficient.

Type: Poster

Authors: Li Ding, Pranam Kolari, Tim Finin, Anupam Joshi, and Yelena Yesha

Date: March 17, 2005

Format: Microsoft PowerPoint (Need a reader? Get one here)

Number of downloads: 457

Access Control: Publicly Available

Available for download as

size: 128000 bytes

Assertions:

1. (Resource) SEMDIS poster (March 2005) is a resource of (Publication) On Homeland Security and the Semantic Web: A Provenance and Trust Aware Inference Framework