Assessing content value for digital publishing through relevance and provenance-based trust

Abstract

Due to the abundance of content on the Web, content authors and publishers have a pressing need for systems that select content that is valuable for them, is trustworthy and is related to their own work. Additionally, the value of their own work needs to be assessed before it is published, to guarantee high value for the consumer. In this doctoral research, we investigate how to use Semantic Web technologies to automatically assess the value of content that is - or is about to be - digitally published. To achieve this, we propose methods to assess the relevance of content to existing publications, retrieve or reconstruct its provenance, and derive a trust assessment from this provenance. We discuss our evaluation methods, and present some preliminary results. © 2013 Springer-Verlag.