Abstract
Link discovery plays a central role in the implementation of the Linked Data vision. In this demo paper, we present SAIM, a tool that aims to support users during the creation of high-quality link specifications. The tool implements a simple but effective workflow to creating initial link specifications. In addition, SAIM implements a variety of state-of-the-art machine-learning algorithms for unsupervised, semi-supervised and supervised instance matching on structured data. We demonstrate SAIM by using benchmark data such as the OAEI datasets.
Chapter PDF
Similar content being viewed by others
References
Auer, S., Lehmann, J., Ngonga Ngomo, A.-C.: Introduction to linked data and its lifecycle on the web. In: Polleres, A., d’Amato, C., Arenas, M., Handschuh, S., Kroner, P., Ossowski, S., Patel-Schneider, P. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 1–75. Springer, Heidelberg (2011)
Morsey, M., Lehmann, J., Auer, S., Stadler, C., Hellmann, S.: DBpedia and the live extraction of structured data from wikipedia. Program: Electronic Library and Information Systems 46, 27 (2012)
Ngonga Ngomo, A.-C.: On link discovery using a hybrid approach. Journal on Data Semantics 1, 203–217 (2012)
Ngonga Ngomo, A.-C., Auer, S.: LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data. In: Proceedings of IJCAI (2011)
Ngonga Ngomo, A.-C., Lehmann, J., Auer, S., Höffner, K.: RAVEN – Active Learning of Link Specifications. In: Proceedings of OM@ISWC, vol. 814 (2011)
Ngonga Ngomo, A.-C., Lyko, K.: EAGLE: Efficient active learning of link specifications using genetic programming. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 149–163. Springer, Heidelberg (2012)
Ngonga Ngomo, A.-C., Lyko, K., Christen, V.: COALA – correlation-aware active learning of link specifications. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 442–456. Springer, Heidelberg (2013)
Nikolov, A., d’Aquin, M., Motta, E.: Unsupervised learning of link discovery configuration. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 119–133. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lyko, K., Höffner, K., Speck, R., Ngomo, AC.N., Lehmann, J. (2013). SAIM – One Step Closer to Zero-Configuration Link Discovery. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds) The Semantic Web: ESWC 2013 Satellite Events. ESWC 2013. Lecture Notes in Computer Science, vol 7955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41242-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-41242-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41241-7
Online ISBN: 978-3-642-41242-4
eBook Packages: Computer ScienceComputer Science (R0)