Abstract
Ontology alignment is a key challenge to allow for interoperability between heterogeneous semantic data sources. Today, most algorithms extract an alignment from a matrix of the pairwise similarities of ontological entities of two ontologies. However, this standard approach has severe disadvantages regarding scalability and is incapable of accounting for global alignment quality criteria that go beyond the aggregation of independent pairwise correspondence evaluations. This paper considers the ontology alignment problem as an optimisation problem that can be addressed using nature-inspired population-based optimisation heuristics. This allows for the deployment of an objective function which can be freely defined to take into account individual correspondence evaluations as well as global alignment constraints. Moreover, such algorithms can easily be parallelised and show anytime behaviour due to their iterative nature. The paper generalises an existing approach to the alignment problem based on discrete particle swarm optimisation, and presents a novel implementation based on evolutionary programming. First experimental results demonstrate feasibility and scalability of the presented approaches.
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.
References
Bellahsene, Z., Bonifati, A., Duchateau, F., Velegrakis, Y.: On Evaluating Schema Matching and Mapping. In: Bellahsene, Z., Bonifati, A., Rahm, E. (eds.) Schema Matching and Mapping. Springer, Heidelberg (2011)
Bock, J., Hettenhausen, J.: Discrete Particle Swarm Optimisation for Ontology Alignment. Information Sciences (2010)
Bock, J., Lenk, A., Dänschel, C.: Ontology Alignment in the Cloud. In: Proc. of the 5th Int. Workshop on Ontology Matching. CEUR Workshop Proceedings, vol. 689, pp. 73–84 (2010), http://ceur-ws.org
Bock, J., Topor, R., Volz, R.: Ontology Merging using Answer Set Programming and Linguistic Knowledge. In: Proc. of the 2nd Int. Workshop on Ontology Matching. CEUR Workshop Proceedings, vol. 304, pp. 301–305 (2007), http://ceur-ws.org
Correa, E.S., Freitas, A.A., Johnson, C.G.: A New Discrete Particle Swarm Algorithm Applied to Attribute Selection in a Bioinformatics Data Set. In: Proc. of the 8th Genetic and Evolutionary Computation Conf. ACM, New York (2006)
Cruz, I.F., Antonelli, F.P., Stroe, C.: Efficient Selection of Mappings and Automatic Quality-driven Combination of Matching Methods. In: Shvaiko, P., Euzenat, J., Giunchiglia, F., Stuckenschmidt, H., Noy, N., Rosenthal, A. (eds.) Proceedings of the 4th International Workshop on Ontology Matching. CEUR Workshop Proceedings, vol. 551, pp. 49–60 (2009), http://ceur-ws.org
Ehrig, M., Euzenat, J.: Relaxed Precision and Recall for Ontology Matching. In: Proc. of the K-CAP Workshop on Integrating Ontologies. CEUR Workshop Proceedings, vol. 156, pp. 25–32 (2005), http://ceur-ws.org
Euzenat, J., Ferrara, A., Hollink, L., Isaac, A., Joslyn, C., Malaisé, V., Meilicke, C., Nikolov, A., Pane, J., Sabou, M., Scharffe, F., Shvaiko, P., Spiliopoulos, V., Stuckenschmidt, H., Šváb-Zamazal, O., Svátek, V., Trojahn, C., Vouros, G., Wang, S.: Results of the Ontology Alignment Evaluation Initiative. In: Proc. of the 4th Int. Workshop on Ontology Matching. CEUR Workshop Proceedings, vol. 551, pp. 73–126 (2009), http://ceur-ws.org
Euzenat, J., Ferrara, A., Meilicke, C., Nikolov, A., Pane, J., Scharffe, F., Shvaiko, P., Stuckenschmidt, H., Šváb-Zamazal, O., Svátek, V., Trojahn dos Santos, C.: Results of the Ontology Alignment Evaluation Initiative. In: Proc. of the 5th Int. Workshop on Ontology Matching. CEUR Workshop Proceedings, vol. 689, pp. 85–117 (2010), http://ceur-ws.org
Fogel, M.J., Owens, L.J., Walsh, A.J.: Artificial Intelligence through Simulated Evolution. Wiley, Chichester (1966)
Hettenhausen, J.: Interactive Multi-Objective Particle Swarm Optimisation with Heatmap Visualisation based User Interface. Master’s thesis, Griffith University (2007)
Jean-Mary, Y.R., Shironoshita, E.P., Kabuka, M.R.: Ontology Matching with Semantic Verification. Web Semantics 7(3), 235–251 (2009)
Joslyn, C.A., Paulson, P., White, A.: Measuring the Structural Preservation of Semantic Hierarchy Alignments. In: Shvaiko, P., Euzenat, J., Giunchiglia, F., Stuckenschmidt, H., Noy, N., Rosenthal, A. (eds.) Proceedings of the 4th International Workshop on Ontology Matching. CEUR Workshop Proceedings, vol. 551, pp. 61–72 (2009), http://ceur-ws.org
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann (April 2001)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. of IEEE Int. Conf. on Neural Networks, vol. 4. IEEE Computer Society (1995)
Kuhn, H.W.: The Hungarian Method for the Assignment Problem. Naval Research Logistics Quarterly 2(1-2), 83–97 (1955)
Li, J., Tang, J., Li, Y., Luo, Q.: RiMOM: A Dynamic Multistrategy Ontology Alignment Framework. IEEE Transactions on Knowledge and Data Engineering 21(8), 1218–1232 (2009)
Meilicke, C., Stuckenschmidt, H.: Repairing Ontology Mappings. In: Proc. of the 22nd AAAI Conf. on Artificial Intelligence. AAAI Press (2007)
Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: Georgakopoulos, D., Agrawal, R., Dittrich, K. (eds.) Proceedings of the 18th International Conference on Data Engineering, pp. 117–128. IEEE Computer Society, Washington, DC, USA (2002)
Mutter, M.: Ontology Alignment durch Evolutionäre Algorithmen. Diplomarbeit, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany (2011)
Niepert, M., Meilicke, C., Stuckenschmidt, H.: A Probabilistic-Logical Framework for Ontology Matching. In: Proc. of the 24th AAAI Conf. on Artificial Intelligence. AAAI Press (2010)
Rahm, E.: Towards Large-Scale Schema and Ontology Matching. In: Bellahsene, Z., Bonifati, A., Rahm, E. (eds.) Schema Matching and Mapping. Springer, Heidelberg (2011)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proc. of IEEE Int. Conf. on Evolutionary Computation. IEEE Computer Society (1998)
Whitley, D.: An overview of evolutionary algorithms: practical issues and common pitfalls. Information and Software Technology 43(14), 817–831 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bock, J., Rudolph, S., Mutter, M. (2012). More Than the Sum of Its Parts – Holistic Ontology Alignment by Population-Based Optimisation. In: Lukasiewicz, T., Sali, A. (eds) Foundations of Information and Knowledge Systems. FoIKS 2012. Lecture Notes in Computer Science, vol 7153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28472-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-28472-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28471-7
Online ISBN: 978-3-642-28472-4
eBook Packages: Computer ScienceComputer Science (R0)