Abstract
Links between knowledge bases build the backbone of the Linked Data Web. In previous works, the combination of the results of time-efficient algorithms through set-theoretical operators has been shown to be very time-efficient for Link Discovery. However, the further optimization of such link specifications has not been paid much attention to. We address the issue of further optimizing the runtime of link specifications by presenting Helios, a runtime optimizer for Link Discovery. Helios comprises both a rewriter and an execution planner for link specifications. The rewriter is a sequence of fixed-point iterators for algebraic rules. The planner relies on time-efficient evaluation functions to generate execution plans for link specifications. We evaluate Helios on 17 specifications created by human experts and 2180 specifications generated automatically. Our evaluation shows that Helios is up to 300 times faster than a canonical planner. Moreover, Helios’ improvements are statistically significant.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Bennett, K., Ferris, M.C., Ioannidis, Y.E.: A genetic algorithm for database query optimization. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 400–407 (1991)
Chaudhuri, S.: An overview of query optimization in relational systems. In: Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS 1998, pp. 34–43. ACM (1998)
Euzenat, J., Ferrara, A., Robert van Hage, W., Hollink, L., Meilicke, C., Nikolov, A., Ritze, D., Scharffe, F., Shvaiko, P., Stuckenschmidt, H., Sváb-Zamazal, O., Trojahn dos Santos, C.: Results of the ontology alignment evaluation initiative 2011. In: OM (2011)
Huber, J., Sztyler, T., Nößner, J., Meilicke, C.: Codi: Combinatorial optimization for data integration: results for oaei 2011. In: OM (2011)
Isele, R., Jentzsch, A., Bizer, C.: Efficient Multidimensional Blocking for Link Discovery without losing Recall. In: WebDB (2011)
Kanne, C.-C., Moerkotte, G.: Histograms reloaded: the merits of bucket diversity. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, pp. 663–674. ACM (2010)
Ngonga Ngomo, A.-C.: Link discovery with guaranteed reduction ratio in affine spaces with minkowski measures. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 378–393. Springer, Heidelberg (2012)
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.: On link discovery using a hybrid approach. Journal on Data Semantics 1, 203–217 (2012)
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)
Niu, X., Rong, S., Zhang, Y., Wang, H.: Zhishi.links results for oaei 2011. In: OM (2011)
Peukert, E., Berthold, H., Rahm, E.: Rewrite techniques for performance optimization of schema matching processes. In: EDBT, pp. 453–464 (2010)
Griffiths Selinger, P., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. In: Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data, SIGMOD 1979, pp. 23–34. ACM, New York (1979)
Shvaiko, P., Euzenat, J.: Ontology matching: State of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)
Silberschatz, A., Korth, H., Sudarshan, S.: Database Systems Concepts, 5th edn. McGraw-Hill, Inc., New York (2006)
Song, D., Heflin, J.: Automatically generating data linkages using a domain-independent candidate selection approach. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 649–664. Springer, Heidelberg (2011)
Xiao, C., Wang, W., Lin, X., Yu, J.X.: Efficient similarity joins for near duplicate detection. In: WWW, pp. 131–140 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ngonga Ngomo, AC. (2014). HELIOS – Execution Optimization for Link Discovery. In: Mika, P., et al. The Semantic Web – ISWC 2014. ISWC 2014. Lecture Notes in Computer Science, vol 8796. Springer, Cham. https://doi.org/10.1007/978-3-319-11964-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-11964-9_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11963-2
Online ISBN: 978-3-319-11964-9
eBook Packages: Computer ScienceComputer Science (R0)