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A K-way spectral partitioning of an ontology for ontology matching

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Abstract

Ontology matching, the process of resolving heterogeneity between two ontologies consumes a lot of computing memory and time. This problem is exacerbated in large ontology matching tasks. To address the problem of time and space complexity in the matching process, ontology partitioning has been adopted as one of the methods, however, most ontology partitioning algorithms either produce incomplete partitions or are slow in the partitioning process hence eroding the benefits of the partitioning. In this paper, we demonstrate that spectral partitioning of an ontology can generate high quality partitions geared towards ontology matching.

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Notes

  1. http://sourceforge.net/projects/owlapi.

  2. https://github.com/ernestojimenezruiz/logmap-matcher.

  3. https://github.com/AgreementMakerLight/AML-Jar.

  4. http://oaei.ontologymatching.org/2016/anatomy/anatomy-dataset.zip.

References

  1. Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)

    Article  Google Scholar 

  2. Rahm, E.: Towards large-scale schema and ontology matching. In: Bellahsene, Z., Bonifati, A., Rahm, E. (eds.) Schema Matching and Mapping, pp. 3–27. Springer, Berlin (2011)

    Chapter  Google Scholar 

  3. Steorts, R., Ventura, S., Sadinle, M., Fienberg, S.: A comparison of blocking methods for record linkage, international conference on privacy in statistical databases. In: International Conference on Privacy in Statistical Databases, pp. 253–268. Springer, Cham (2014)

    Google Scholar 

  4. Karlapalem, K., Li, Q.: Framework for class partitioning in object-oriented databases. Distrib. Parallel Databases 8(3), 333–366 (2000)

    Article  Google Scholar 

  5. Curino, C., Jones, E., Zhang, Y., Madden, S.: Schism: a workload-driven approach to database replication and partitioning. Proc. VLDB Endow. 3(1–2):48–57 (2010). http://dl.acm.org/citation.cfm?id=1920841.1920853%5Cnpapers3://publication/doi/10.14778/1920841.1920853

    Article  Google Scholar 

  6. Hamdi, F., Safar, B., Reynaud, C., Zargayouna, H.: Alignment-based Partitioning of Large-scale Ontologies, Advances in Knowledge discovery Management. Studies in Computation Intelligence. Springer, Heidelberg, pp. 251–269 (2010)

    Chapter  Google Scholar 

  7. Grau, B.C., Parsia, B., Sirin, E., Kalyanpur, A.: Modularity and Web Ontologies. In: Tenth International Conference on Principles of Knowledge Representation and Reasoning KR2006, pp. 198–209 (2006)

  8. Doran, P., Tamma, V., Iannone, L.: Ontology Module Extraction for Ontology Reuse: An Ontology Engineering Perspective. In: Proceedings of 16th ACM Conference in Information and Knowledge Management, pp. 61–70 (2007)

  9. Chan, P.K., Schlag, M.D., Zien, J.Y.: Spectral K-way ratio-cut partitioning and clustering. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. pp. 1088–1096 (1994)

    Article  Google Scholar 

  10. Spielman, D.A., Teng, S.H.: Spectral partitioning works: planar graphs and finite element meshes. Linear Algebr. Appl. 421(2–3), 284–305 (2007)

    Article  MathSciNet  Google Scholar 

  11. Hagen, L., Member, S., Kahng, A.B.: New spectral methods for ratio cut partitioning and clustering. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 11(9):1074–1085 (1992)

    Article  Google Scholar 

  12. Malik, J., Belongie, S., Leung, T.K., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vis. 43(1), 7–27 (2001)

    Article  Google Scholar 

  13. Pathak, J., Johnson, T.M., Chute, C.G.: Survey of modular ontology techniques and their applications in the biomedical domain. Integr. Comput. Aided Eng. 16(3), 225–242 (2009)

    Article  Google Scholar 

  14. Grau, B.C., Parsia, B., Sirin, E., Kalyanpur, A.: Automatic Partitioning of OWL ontologies using e-connections. In: CEUR Workshop Proceedings (2005)

  15. Hu, W., Zhao, Y., Qu, Y.: Partition-based block matching of large class hierarchies. In: 1st Asian Semantic Web Conference (ASWC 2006), pp. 72–83 (2006)

    Chapter  Google Scholar 

  16. Grau, B.C., Horrocks, I., Kazakov, Y., Sattler, U.: A logical framework for modularity of ontologies. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 298–303 (2007)

  17. Grau, B.C., Horrocks, I., Kazakov, Y., Sattler, U.: Modular reuse of ontologies: theory and practice. J. Artif. Intell. Res. 31, 273–318 (2008)

    Article  MathSciNet  Google Scholar 

  18. Del Vescovo, C., Parsia, B., Sattler, U., Schneider, T.: The modular structure of an ontology: atomic decomposition. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 2232–2237 (2011)

  19. Fahad, M.: Initial results for ontology matching workshop 2015 DKP-AOM : results for OAEI 2015. In: CEUR Workshop Proceedings (2015)

  20. Kuśnierczyk, W.: Taxonomy-based partitioning of the Gene Ontology. J. Biomed. Inform. 41(2), 282–292 (2008)

    Article  Google Scholar 

  21. Schlicht, A., Stuckenschmidt, H.: A flexible partitioning tool for large ontologies. In: Proceedings—2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008, pp. 482–488 (2008)

  22. Algergawy, A., Babalou, S., Klan, F., König-ries, B.: OAPT : a tool for ontology analysis and partitioning. In: Proceedings of 19th International Conference on Extending Database Technology, pp. 644–647 (2016)

  23. Do, H.H., Rahm, E.: Matching large schemas: approaches and evaluation. Inform. Syst. pp. 857–885 (2007)

    Article  Google Scholar 

  24. Rahm, E., Do, H.-H., Maßmann, S.: Matching large XML schemas. ACM SIGMOD Rec. 33(4), 26 (2004)

    Article  Google Scholar 

  25. Algergawy, A., Massmann, S., Rahm, E.: A clustering-based approach for large-scale ontology matching. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 415–428 (2011)

    Chapter  Google Scholar 

  26. Algergawy, A., Klan, F., Konig-Ries, B.: Partitioning-based ontology matching approaches: a comparative analysis. CEUR Workshop Proc. 1317, 180–181 (2014)

    Google Scholar 

  27. Hamerly, G., Elkan, C.: Learning the k in kmeans. In: Advances in Neural Information Processing Systems, vol. 17, pp. 1–8 (2004). https://www.books.nips.cc/papers/files/nips16/NIPS2003_AA36.pdf%5Cn; https://www.books.google.com/books?hl=en&lr=&id=0F-9C7K8fQ8C&oi=fnd&pg=PA281&dq=Learning+the+k+in+k-means&ots=TGLvqYQa40&sig=SDu4cZ9TCeU8a5MoG1uMcRLQGFE

  28. Tobergte, D.R., Curtis, S.: Semantic web and semantic web services (2013)

  29. Schlicht, A., Stuckenschmidt, H.: Criteria-based partitioning of large ontologies. In: Proceedings of the 4th International Conference on Knowledge Capture, pp. 171–172 (2007)

  30. Sánchez, D., Batet, M., Isern, D., Valls, A.: Ontology-based semantic similarity: a new feature-based approach. Expert Syst. Appl. 39(9), 7718–7728 (2012)

    Article  Google Scholar 

  31. Bollegala, D., Matsuo, Y., Ishizuka, M.: A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2—EMNLP ’09, p. 803 (2009)

  32. Al-mubaid, H., Nguyen, H.A.: A cluster-based approach for semantic similarity in the biomedical domain. In: Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, pp. 2713–2717 (2006)

  33. Li, Y., Bandar, Z.A., Mclean, D.: An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans. Knowl. Data Eng. 4, 871–882 (2003)

    Google Scholar 

  34. Ogren, P.V., Cohen, K.B., Acquaah-Mensah, G.K., Eberlein, J., Hunter, L.: The compositional structure of Gene Ontology terms. In: Pacific Symposium on Biocomputing, pp. 214–25 (2004)

  35. Hamacher, H., Leberling, H., Zimmermann, H.-J.: Sensitivity analysis in fuzzy linear programming. Fuzzy Sets Syst. 1(4), 269–281 (1978)

    Article  MathSciNet  Google Scholar 

  36. Stoilos, G., Stamou, G., Kollias, S.: A string metric for ontology alignment. In: International Semantic Web Conference. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 624–637 (2005)

    Chapter  Google Scholar 

  37. Winkler, W.E.: The State of Record Linkage and Current Research Problems. In: Statistical Research Division US Census Bureau, pp. 1–15 (1999)

  38. Yang, X.S.: Firefly algorithms for multimodal optimization. In International Symposium on Stochastic Algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 169–178 (2009)

    Chapter  Google Scholar 

  39. Palmer, M.: Verb semantics and lexical Zhibiao W u. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico, pp. 133–138 (1994)

  40. Mohar, B.: Some applications of Laplace eigenvalues of graphs. In: Hahn, G., Sabidussi, G. (eds.) Graph Symmetry: Algebraic Methods and Applications, pp. 225–275. Springer, Dordrecht (1991)

    Google Scholar 

  41. Luxburg, U.V.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2006)

    Article  MathSciNet  Google Scholar 

  42. Mohar, B.: The Laplacian spectrum of graphs. In: Proceedings of 6th Quadrennial International Conference on Theory and Applications of Graphs, pp. 871–898 (1988)

  43. Hall, K.M.: An r-dimensional quadratic placement algorithm. Manag. Sci. 17(3), 219–229 (1970)

    Article  Google Scholar 

  44. Ding, C.H.Q., He, X., Zhab, H., Gu, M., Simon, H.D.: A min-max cut algorithm for graph partitioning and data clustering. In: IEEE Proceedings 2001 IEEE International Conference on Data Mining, pp. 107–114 (2001)

  45. Alpert, C.J., Yao, S.-z.: Spectral partitioning: the more eigenvectors, the better. In: IEEE 32nd Design Automation Conference, pp. 195–200 (1995)

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  1. Makerere University, Kampala, Uganda

    Peter Ochieng & Swaib Kyanda

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  1. Peter Ochieng
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  2. Swaib Kyanda
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Correspondence to Peter Ochieng.

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Ochieng, P., Kyanda, S. A K-way spectral partitioning of an ontology for ontology matching. Distrib Parallel Databases 36, 643–673 (2018). https://doi.org/10.1007/s10619-018-7222-8

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