Abstract
Spreadsheets compose a notably large and valuable dataset of documents within the enterprise settings and on the Web. Although spreadsheets are intuitive to use and equipped with powerful functionalities, extracting and reusing data from them remains a cumbersome and mostly manual task. Their greatest strength, the large degree of freedom they provide to the user, is at the same time also their greatest weakness, since data can be arbitrarily structured. Therefore, in this paper we propose a supervised learning approach for layout recognition in spreadsheets. We work on the cell level, aiming at predicting their correct layout role, out of five predefined alternatives. For this task we have considered a large number of features not covered before by related work. Moreover, we gather a considerably large dataset of annotated cells, from spreadsheets exhibiting variability in format and content. Our experiments, with five different classification algorithms, show that we can predict cell layout roles with high accuracy. Subsequently, in this paper we focus on revising the classification results, with the aim of repairing misclassifications. We propose a sophisticated approach, composed of three steps, which effectively corrects a reasonable number of inaccurate predictions.
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
Koci, E., Thiele, M., Romero, O., Lehner, W.: A machine learning approach for layout inference in spreadsheets. In: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016), KDIR, Porto, Portugal, 9–11 November 2016, vol. 1, pp. 77–88 (2016)
Chen, Z., Cafarella, M.: Automatic web spreadsheet data extraction. In: SSW 2013, p. 1. ACM (2013)
Barik, T., Lubick, K., Smith, J., Slankas, J., Murphy-Hill, E.: FUSE: a reproducible, extendable, internet-scale corpus of spreadsheets. In: MSR 2015 (2015)
Hermans, F., Murphy-Hill, E.: Enron’s spreadsheets and related emails: a dataset and analysis. In: Proceedings of ICSE 2015. IEEE (2015)
Fisher, M., Rothermel, G.: The EUSES spreadsheet corpus: a shared resource for supporting experimentation with spreadsheet dependability mechanisms. In: SIGSOFT 2005, vol. 30, pp. 1–5. ACM (2005)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth, Belmont (1984)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., Boston (1993)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Vapnik, V.: Estimation of Dependences Based on Empirical Data. Springer Series in Statistics. Springer-Verlag New York, Inc., New York (1982)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods - Support Vector Learning. MIT Press (1998)
Chen, Z., Cafarella, M.: Integrating spreadsheet data via accurate and low-effort extraction. In: SIGKDD 2014, pp. 1126–1135. ACM (2014)
Adelfio, M.D., Samet, H.: Schema extraction for tabular data on the web. In: VLDB 2013, vol. 6, pp. 421–432 (2013)
Abraham, R., Erwig, M.: Header and unit inference for spreadsheets through spatial analyses. In: VL/HCC 2004, pp. 165–172. IEEE (2004)
Eberius, J., Werner, C., Thiele, M., Braunschweig, K., Dannecker, L., Lehner, W.: DeExcelerator: a framework for extracting relational data from partially structured documents. In: CIKM 2013, pp. 2477–2480. ACM (2013)
Wang, Y., Hu, J.: A machine learning based approach for table detection on the web. In: WWW 2002, pp. 242–250. ACM (2002)
Crestan, E., Pantel, P.: Web-scale table census and classification. In: WSDM 2011, pp. 545–554. ACM (2011)
Eberius, J., Braunschweig, K., Hentsch, M., Thiele, M., Ahmadov, A., Lehner, W.: Building the dresden web table corpus: a classification approach. In: BDC 2015. IEEE/ACM (2015)
Acknowledgments
This research has been funded by the European Commission through the Erasmus Mundus Joint Doctorate “Information Technologies for Business Intelligence - Doctoral College” (IT4BI-DC).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Koci, E., Thiele, M., Romero, O., Lehner, W. (2019). Cell Classification for Layout Recognition in Spreadsheets. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2016. Communications in Computer and Information Science, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-319-99701-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-99701-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99700-1
Online ISBN: 978-3-319-99701-8
eBook Packages: Computer ScienceComputer Science (R0)