A K-way spectral partitioning of an ontology for ontology matching
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.
Keywords
Spectral Partitioning Ontology Signature MatchingReferences
- 1.Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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
- 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)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)Google Scholar
- 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)Google Scholar
- 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)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)MathSciNetCrossRefMATHGoogle 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)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)CrossRefMATHGoogle 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)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)Google Scholar
- 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)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)Google Scholar
- 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)MathSciNetMATHGoogle 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)Google Scholar
- 19.Fahad, M.: Initial results for ontology matching workshop 2015 DKP-AOM : results for OAEI 2015. In: CEUR Workshop Proceedings (2015)Google Scholar
- 20.Kuśnierczyk, W.: Taxonomy-based partitioning of the Gene Ontology. J. Biomed. Inform. 41(2), 282–292 (2008)CrossRefGoogle 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)Google Scholar
- 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)Google Scholar
- 23.Do, H.H., Rahm, E.: Matching large schemas: approaches and evaluation. Inform. Syst. pp. 857–885 (2007)Google Scholar
- 24.Rahm, E., Do, H.-H., Maßmann, S.: Matching large XML schemas. ACM SIGMOD Rec. 33(4), 26 (2004)CrossRefGoogle 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)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)Google Scholar
- 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)Google Scholar
- 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)CrossRefGoogle 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)Google Scholar
- 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)Google Scholar
- 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)Google Scholar
- 35.Hamacher, H., Leberling, H., Zimmermann, H.-J.: Sensitivity analysis in fuzzy linear programming. Fuzzy Sets Syst. 1(4), 269–281 (1978)MathSciNetCrossRefMATHGoogle 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)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)Google Scholar
- 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)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)Google Scholar
- 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)MathSciNetCrossRefGoogle 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)Google Scholar
- 43.Hall, K.M.: An r-dimensional quadratic placement algorithm. Manag. Sci. 17(3), 219–229 (1970)CrossRefMATHGoogle 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)Google Scholar
- 45.Alpert, C.J., Yao, S.-z.: Spectral partitioning: the more eigenvectors, the better. In: IEEE 32nd Design Automation Conference, pp. 195–200 (1995)Google Scholar
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