Interest in web-based syndication systems has been growing as information streams onto the web at an increasing
rate. Technologies, like the standard Semantic Web languages RDF and OWL, make it possible to create expressive
representations of the content of publications and subscriptions in a syndication framework. Because these languages
are based in description logics, this representation allows the application to reasoning to make more precise matching
of user interests with published information. A challenge to this approach is that the consistency of the underlying
knowledge base must be maintained for these techniques to work. With the frequent addition of information from
new publications, it is likely that inconsistencies will arise. There are many potential mechanisms for choosing
which inconsistent information to discard from the KB to regain consistency; in the case of news syndication, we
argue keeping the most trusted information is important for generating the most valuable matches. Thus, in this
article, we present algorithms for belief-base revision, and specifically look at the user’s trust in the information
sources as a metric for deciding what to keep in the KB and what to remove.
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