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2014 IEEE International Conference on Web Services (ICWS) (2014)
Anchorage, AK, USA
June 27, 2014 to July 2, 2014
ISBN: 978-1-4799-5053-9
pp: 105-112
The automatic extraction of metadata and other information from scholarly documents is a common task in academic digital libraries, search engines, and document management systems to allow for the management and categorization of documents and for search to take place. A Web-accessible API can simplify this extraction by providing a single point of operation for extraction that can be incorporated into multiple document workflows without the need for each workflow to implement and support its own extraction functionality. In this paper, we describe CiteSeerExtractor, a RESTful API for scholarly information extraction that exploits the fact that there is duplication in scholarly big data and makes use of a near duplicate matching backend. The backend stores previously extracted metadata and avoids extracting metadata from a document if it has already been extracted before. We describe the design, implementation, and functionality of CiteSeerExtractor and show how the duplicate document matching results in a difference of 8.46% in the time required to extract header and citation information from approximately 3.5 million documents compared to a baseline.
Data mining, Big data, Web servers, Information retrieval, Databases, Hamming distance

K. Williams, L. Li, M. Khabsa, J. Wu, P. C. Shih and C. L. Giles, "A Web Service for Scholarly Big Data Information Extraction," 2014 IEEE International Conference on Web Services (ICWS), Anchorage, AK, USA, 2014, pp. 105-112.
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