Bibliographic metadata plays a key role in scientific litera- ture, not only to summarise and establish the facts of the publication record, but also to track citations between pub- lications and hence to establish the impact of individual ar- ticles within the literature. Commercial secondary publish- ers 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 avail- able 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 es- tablished. In this paper we explain
The question of citation behavior has always intrigued scientists from various disciplines. While general citation patterns have been widely studied in the literature we develop the notion of citation projection graphs by investigating the citations among the publi- cations that a given paper cites. We investigate how patterns of citations vary between various scientific disciplines and how such patterns reflect the scientific impact of the paper. We find that id- iosyncratic citation patterns are characteristic for low impact pa- pers; while narrow, discipline-focused citation patterns are com- mon for medium impact papers. Our results show that crossing- community, or bridging citation patters are high risk and high re- ward since such patterns are characteristic for both low and high impact papers. Last, we observe that recently citation networks are trending toward more bridging and interdisciplinary forms.