Abstract
The abundance of the data in the Internet facilitates the improvement of extraction and processing tools. The trend in the open data publishing encourages the adoption of structured formats like CSV and RDF. However, there is still a plethora of unstructured data on the Web which we assume contain semantics. For this reason, we propose an approach to derive semantics from web tables which are still the most popular publishing tool on the Web. The paper also discusses methods and services of unstructured data extraction and processing as well as machine learning techniques to enhance such a workflow. The eventual result is a framework to process, publish and visualize linked open data. The software enables tables extraction from various open data sources in the HTML format and an automatic export to the RDF format making the data linked. The paper also gives the evaluation of machine learning techniques in conjunction with string similarity functions to be applied in a tables recognition task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Babyak, M.A.: What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic Medicine 66(3), 411–421 (2004)
Cafarella, M., Wu, E., Halevy, A., Zhang, Y., Wang, D.: Webtables: exploring the power of tables on the web. In: VLDB 2008 (2008)
Crestan, E., Pantel, P.: Web-scale table census and classification. In: ACM WSDM 2011 Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 545–554 (2011)
Embley, D., Tao, C., Liddle, S.: Automating the extraction of data from html tables with unknown structure. Data and Knowledge Engineering 54, 3–28 (2005). Special issue: ER
Ermilov, I., Auer, S., Stadler, C.: User-driven semantic mapping of tabular data. In: Proceedings of the 9th International Conference on Semantic Systems, pp. 105–112. ACM (2013)
Haase, P., Schmidt, M., Schwarte, A.: The information workbench as a self-service platform for linked data applications. In: COLD. Citeseer (2011)
Hu, J., Kashi, R., Lopresti, D., Wilfong, G.: Evaluating the performance of table processing algorithms. International Journal on Document Analysis and Recognition 4, 140–153 (2002)
Hurst, M.: A constraint-based approach to table structure derivation. In: Proceedings of International Conference on Document Analysis and Recognition (ICDAR 2003), pp. 911–915 (2003)
Kolchin, M., Kozlov, F.: A template-based information extraction from web sites with unstable markup. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 89–94. Springer, Heidelberg (2014)
Mitchell, T.: Machine Learning. McGraw-Hill Science/Engineering/Math (1997)
Mouromtsev, D., Vlasov, V., Parkhimovich, O., Galkin, M., Knyazev, V.: Development of the st. petersburg’s linked open data site using information workbench. In: 14th FRUCT Proceedings, pp. 77–82 (2013)
Rokach, L., Maimon, O.: Data mining with decision trees: theory and applications. World Scientific Pub. Co., Inc. (2008)
Silva, A., Jorge, A., Torgo, L.: Design of an end-to-end method to extract information from tables. International Journal of Document Analysis and Recognition (IJDAR) 8, 144–171 (2006)
Tijerino, Y., Embley, D., Lonsdale, D., Ding, Y., Nagy, G.: Towards ontology generation from tables. World Wide Web 8, 261–285 (2005)
Tou, J., Gonzalez, R.: Pattern Recognition Principles. University of Southern California (1974)
Ukkonen, E.: Approximate string matching with q-grams and maximal matches. Theoretical Computer Science, 191–211 (1992)
Vorontsov, K.: Machine learning methods (2009). http://www.machinelearning.ru
Wang, Y., Hu, J.: A machine learning based approach for table detection on the web. In: Proceedings of the 11th WWW, pp. 242–250 (2002)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Galkin, M., Mouromtsev, D., Auer, S. (2015). Identifying Web Tables: Supporting a Neglected Type of Content on the Web. In: Klinov, P., Mouromtsev, D. (eds) Knowledge Engineering and Semantic Web. KESW 2015. Communications in Computer and Information Science, vol 518. Springer, Cham. https://doi.org/10.1007/978-3-319-24543-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-24543-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24542-3
Online ISBN: 978-3-319-24543-0
eBook Packages: Computer ScienceComputer Science (R0)