Abstract
Semantic enrichment of scientific publications has an increasing impact on scholarly communication. This document describes our contribution to Semantic Publishing Challenge 2016, which aims at investigating novel approaches for improving scholarly publishing through semantic technologies. We participated in Task 2 of this challenge, which requires the extraction of information from the content of a paper given as PDF. The extracted information allows answering queries about the paper’s internal organisation and the context in which it was written. We build upon our contribution to the previous edition of the challenge, where we categorised meta-data, such as authors and affiliations, and extracted funding information. Here we use unsupervised machine learning techniques in order to extend the analysis of the logical structure of the document as to identify section titles and captions of figures and tables. Furthermore, we employ clustering techniques to create the hierarchical table of contents of the article. Our system is modular in nature and allows a separate training of different stages on different training sets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
https://svn.know-center.tugraz.at/opensource/projects/code/trunk User: Anonymous, empty password.
- 4.
- 5.
- 6.
References
Aiello, M., Monz, C., Todoran, L., Worring, M.: Document understanding for a broad class of documents. Int. J. Doc. Anal. Recogn. 5(1), 1–16 (2002)
Berger, A.L., Pietra, V.J.D., Pietra, S.A.D.: A maximum entropy approach to natural language processing. Comput. Linguist. 22(1), 39–71 (1996)
Iorio, A.D., Lange, C., Dimou, A., Vahdati, S.: Semantic publishing challenge – assessing the quality of scientific output by information extraction and interlinking. SemWebEval 2015. CCIS, vol. 548, pp. 65–80. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25518-7_6
Gao, L., Tang, Z., Lin, X., Liu, Y., Qiu, R., Wang, Y.: Structure extraction from PDF-based book documents. In: Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, pp. 11–20 (2011)
Kern, R., Jack, K., Hristakeva, M., Granitzer, M.: TeamBeam - meta-data extraction from scientific literature. In: 1st International Workshop on Mining Scientific Publications (2012)
Kern, R., Klampfl, S.: Extraction of references using layout and formatting information from scientific articles. D-Lib Mag. 19(9/10), 2 (2013)
Klampfl, S., Granitzer, M., Jack, K., Kern, R.: Unsupervised document structure analysis of digital scientific articles. Int. J. Digit. Libr. 14(3–4), 83–99 (2014)
Klampfl, S., Kern, R.: An unsupervised machine learning approach to body text and table of contents extraction from digital scientific articles. In: Aalberg, T., Papatheodorou, C., Dobreva, M., Tsakonas, G., Farrugia, C.J. (eds.) TPDL 2013. LNCS, vol. 8092, pp. 144–155. Springer, Heidelberg (2013)
Klampfl, S., Kern, R.: Machine learning techniques for automatically extracting contextual information from scientific publications. In: Gandon, F., et al. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 105–116. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25518-7_9
Kröll, M., Klampfl, S., Kern, R.: Towards a marketplace for the scientific community: accessing knowledge from the computer science domain. D-Lib Mag. 20(11/12), 10 (2014)
Lin, X.: Header and footer extraction by page-association. In: Proceedings of SPIE vol. 5010, pp. 164–171 (2002)
Liu, Y., Mitra, P., Giles, C.L.: Identifying table boundaries in digital documents via sparse line detection. In: Proceeding of the 17th ACM Conference on Information and Knowledge Mining CIKM 2008, pp. 1311–1320. ACM Press (2008)
Ratnaparkhi, A.: Maximum entropy models for natural langual ambiguity resolution. Ph.D. thesis (1998)
Acknowledgements
The presented work was in part developed within the CODE project (grant no. 296150) and within the EEXCESS project (grant no. 600601) funded by the EU FP7, as well as the TEAM IAPP project (grant no. 251514) within the FP7 People Programme. The Know-Center is funded within the Austrian COMET Program – Competence Centers for Excellent Technologies – under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry of Economy, Family and Youth and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Klampfl, S., Kern, R. (2016). Reconstructing the Logical Structure of a Scientific Publication Using Machine Learning. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds) Semantic Web Challenges. SemWebEval 2016. Communications in Computer and Information Science, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-46565-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-46565-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46564-7
Online ISBN: 978-3-319-46565-4
eBook Packages: Computer ScienceComputer Science (R0)