Abstract
CERMINE is a comprehensive open source system for extracting structured metadata and references from born-digital scientific literature. Among other information, the system is able to extract information related to the context the article was written in, such as the authors and their affiliations, the relations between them or references to other articles. Extracted information is presented in a structured, machine-readable form. CERMINE is based on a modular workflow, whose loosely coupled architecture allows for individual components evaluation and adjustment, enables effortless improvements and replacements of independent parts of the algorithm and facilitates future architecture expanding. The implementation of the workflow is based mostly on supervised and unsupervised machine-learning techniques, which simplifies the procedure of adapting the system to new document layouts and styles. In this paper we outline the overall workflow architecture, describe key aspects of the system implementation, provide details about training and adjusting of individual algorithms, and finally report how CERMINE was used for extracting contextual information from scientific articles in PDF format in the context of ESWC 2015 Semantic Publishing Challenge. CERMINE system is available under an open-source licence and can be accessed at http://cermine.ceon.pl.
Keywords
- Extract Individual Characters
- Reference String
- Page Segmentation
- Stringent Association
- Reference Parsing
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Dublin Core. http://dublincore.org/
iText. http://itextpdf.com/
Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM TIST 2(3), 27 (2011)
Giles, C.L., Bollacker, K.D., Lawrence, S.: Citeseer: An automatic citation indexing system. In: Proceedings of the 3rd ACM International Conference on Digital Libraries, pp. 89–98 (1998)
McCallum, A., Nigam, K., Rennie, J.: Automating the construction of internet portals with machine learning. Inf. Retrieval 3, 127–163 (2000)
McCallum, A.K.: MALLET: A Machine Learning for Language Toolkit (2002)
O’Gorman, L.: The document spectrum for page layout analysis. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1162–1173 (1993)
Tkaczyk, D., Szostek, P., Bolikowski, L.: GROTOAP2 - the methodology of creating a large ground truth dataset of scientific articles. D-Lib Magazine (2014)
Tkaczyk, D., Szostek, P., Fedoryszak, M., Dendek, P.J., Bolikowski, L.: CERMINE: automatic extraction of structured metadata from scientific literature. Int. J. Doc. Anal. Recogn. (IJDAR), 1–19 (2015). http://dx.doi.org/10.1007/s10032-015-0249-8. doi:10.1007/s10032-015-0249-8
Tkaczyk, D., et al.: Cermine: Cermine 1.6 (2015). http://dx.doi.org/10.5281/zenodo.17594
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Tkaczyk, D., Bolikowski, Ł. (2015). Extracting Contextual Information from Scientific Literature Using CERMINE System. In: Gandon, F., Cabrio, E., Stankovic, M., Zimmermann, A. (eds) Semantic Web Evaluation Challenges. SemWebEval 2015. Communications in Computer and Information Science, vol 548. Springer, Cham. https://doi.org/10.1007/978-3-319-25518-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-25518-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25517-0
Online ISBN: 978-3-319-25518-7
eBook Packages: Computer ScienceComputer Science (R0)