Bibliographic metadata plays a key role in scientific literature, not only to summarise and establish the facts of the publication record, but also to track citations between publications and hence to establish the impact of individual articles within the literature. Commercial secondary publishers have typically taken on the role of rekeying, mining and analysing this huge corpus of linked data, but as the primary literature has moved to the world of the digital repository, this task is now undertaken by new services such as Citeseer, Citebase or Google Scholar. As institutional and subject-based repositories proliferate and Open Access mandates increase, more of the literature will become openly available in well managed data islands containing a much greater amount of detailed bibliometric metadata in formats such as RDF. Through the use of efficient extraction and inference techniques, complex relations between data items can be established. In this paper we explain the importance of the co-relation in enabling new techniques to rate the impact of a paper or author within a large corpus of publications
