Peer-Reviewed Journal Details
Mandatory Fields
Groza, T,Grimnes, GA,Handschuh, S,Decker, S
2013
January
Knowledge And Information Systems
From raw publications to Linked Data
Published
()
Optional Fields
Metadata extraction Support vector machines Conditional random fields Linked data SOCIAL NETWORK EVOLUTION
34
1
21
The continuous development of the Linked Data Web depends on the advancement of the underlying extraction mechanisms. This is of particular interest for the scientific publishing domain, where currently most of the data sets are being created manually. In this article, we present a Machine Learning pipeline that enables the automatic extraction of heading metadata (i.e., title, authors, etc) from scientific publications. The experimental evaluation shows that our solution handles very well any type of publication format and improves the average extraction performance of the state of the art with around 4%, in addition to showing an increased versatility. Finally, we propose a flexible Linked Data-driven mechanism to be used both for refining and linking the automatically extracted metadata.
DOI 10.1007/s10115-011-0473-6
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