A PROV-O based approach to web content provenance

Data provenance is currently a hot issue, and many webpages still lack provenance annotation. PROV-O is an emerging W3C recommendation for a provenance data model and language. In this paper, through the analysis of web document derivation, we define a document as an entity and extract a number of semantic properties about document features. A semantic similarity clustering method is used to determine the relationship during the changes of documents. Feature words variation and the responsible person can be found with the aid of PROV-O. Then, taking “genetically modified” news Webpages as test documents, we verify the proposed approach.