Semantify CEUR-WS Proceedings: Towards the Automatic Generation of Highly Descriptive Scholarly Publishing Linked Datasets
Rich and fine-grained semantic information describing varied aspects of scientific productions is essential to support their diffusion as well as to properly assess the quality of their output. To foster this trend, in the context of the ESWC2014 Semantic Publishing Challenge, we present a system that automatically generates rich RDF datasets from CEUR-WS workshop proceedings. Proceedings are analyzed through a sequence of processing phases. SVM classifiers complemented by heuristics are used to annotate missing CEUR-WS markups. Annotations are then linked to external datasets like DBpedia and Bibsonomy. Finally, the data is modeled and published as an RDF graph. Our system is provided as an on-line Web service to support on-the-fly RDF generation. In this paper we describe the system and present its evaluation following the procedure set by the organizers of the challenge.
KeywordsSemantic Web Information extraction Scholarly publishing Open Linked Data
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