SPedia: A semantics based repository of scientific publications data

Abstract

There is a noticeable increase in the number of scientific publications. These publications are being published by different publishers. Springer is one of those publishers which has published more than nine million scientific documents. SpringerLink is the portal providing the gateway to searching and accessing these published scientific documents. The structure, as well as the way, the contents are presented on the portal, provides valuable information about documents metadata such as author, ISBN, references, articles, chapters. However, this metadata is understandable by human in such a way that it facilitates the keyword-based searches through SpringerLink portal. At the same time this huge data about scientific documents is in silence as it is neither open nor linked to other datasets. To address these issues, we have created a semantics based repository called SPedia which consists of semantically enriched data about documents published by Springer. Currently, SPedia datasets consist of more than 300 million RDF triples. In this paper we describe SPedia and examine the quality of its extracted data by performing semantically enriched queries. The results show that SPedia facilitates the users to put sophisticated queries by employing semantic Web techniques instead of keyword-based searches. In addition, SPedia datasets can be utilized to link to other datasets available in the Linked Open Data (LOD) cloud.