Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-24T23:50:07.814Z Has data issue: false hasContentIssue false

An overview of current ontology meta-matching solutions

Published online by Cambridge University Press:  12 November 2012

Jorge Martinez-Gil
Affiliation:
Department of Computer Language and Computing Sciences, University of Málaga, Boulevard Louis Pasteur 35, 29071 Málaga, Spain; e-mail: jorgemar@unex.es, jfam@lcc.uma.es
José F. Aldana-Montes
Affiliation:
Department of Computer Language and Computing Sciences, University of Málaga, Boulevard Louis Pasteur 35, 29071 Málaga, Spain; e-mail: jorgemar@unex.es, jfam@lcc.uma.es

Abstract

Nowadays, there are a lot of techniques and tools for addressing the ontology matching problem; however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thresholds in scenarios where accurate ontology matching is necessary. We think that an overview of the problem and an analysis of the existing state-of-the-art solutions will help researchers and practitioners to identify the most appropriate specific features and global strategies in order to build more accurate and dynamic systems following this paradigm.

Type
Articles
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aizawa, A. 2003. An information-theoretic perspective of tf–idf measures. Information Processing and Management 39(1), 4565.CrossRefGoogle Scholar
Aumueller, D., Hai Do, H., Massmann, S., Rahm, E. 2005. Schema and ontology matching with COMA++. In Proceedings of the SIGMOD Conference, Baltimore, MD, USA, 906–908.Google Scholar
Baeza-Yates, R., Ribeiro-Neto, B. 1999. Modern Information Retrieval. ACM Press/Addison-Wesley.Google Scholar
Berlin, J., Motro, A. 2002. Database schema matching using machine learning with feature selection. In Proceedings of the International Conference on Advanced Information Systems Engineering CAiSE'02. Springer-Verlag, 452–466.Google Scholar
Bernstein, P., Melnik, S. 2004. Meta data management. In Proceedings of the International Conference on Data Engineering ICDE'04, IEEE Computer Society, 875.Google Scholar
Buckland, M., Gey, F. 1994. The relationship between recall and precision. Journal of the American Society for Information Science 45(1), 1219.3.0.CO;2-L>CrossRefGoogle Scholar
Cabral, L., Domingue, J., Motta, E., Payne, T., Hakimpour, F. 2004. Approaches to semantic web services: an overview and comparisons. In Proceedings of the European Semantic Web Conference ESWC'04, Bussler, C., Davies, J., Fensel, D. & Studer, R. (eds). Springer-Verlag, 225–239.Google Scholar
Cilibrasi, R., Vitanyi, P. 2007. The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370383.CrossRefGoogle Scholar
Chen, H., Perich, F., Finin, T., Joshi, A. 2004. SOUPA: Standard Ontology for Ubiquitous and Pervasive Applications. In Proceedings of the Conference on Mobile and Ubiquitous Systems MobiQuitous'04, Cambridge, MA, USA, 258–267.Google Scholar
Choi, C., Song, I., Han, H. 2006. A survey on ontology mapping. ACM Sigmod Record 35(3), 3441.CrossRefGoogle Scholar
Cohen, D., Litsyn, S., Zemor, G. 1996. On greedy algorithms in coding theory. IEEE Transactions on Information Theory 42(6), 20532057.CrossRefGoogle Scholar
Doan, A., Madhavan, J., Dhamankar, R., Domingos, P., Halevy, A. 2003. Learning to match ontologies on the semantic web. The International Journal on Very Large Data Bases 12(4), 303319.CrossRefGoogle Scholar
Domshlak, C., Gal, A., Roitman, H. 2007. Rank aggregation for automatic schema matching. IEEE Transactions on Knowledge and Data Engineering 19(4), 538553.CrossRefGoogle Scholar
Duchateau, F., Bellahsene, Z., Coletta, R. 2008. A flexible approach for planning schema matching algorithms. In Proceedings of On The Move Conferences (1) OTM'08. Springer-Verlag, 249–264.Google Scholar
Duchateau, F., Coletta, R., Bellahsene, Z., Miller, R. J. 2009. (Not) yet another matcher. In Proceedings of the ACM Conference on Information and Knowledge Management CIKM'09, Hong Kong, China, 1537–1540.Google Scholar
Eckert, K., Meilicke, C., Stuckenschmidt, H. 2009. Improving ontology matching using meta-level learning. In Proceedings of the European Semantic Web Conference ESWC'09. Springer-Verlag, 158–172.Google Scholar
Ehrig, M. 2006. Ontology Alignment: Bridging the Semantic Gap. Springer-Verlag.Google Scholar
Ehrig, M., Sure, Y. 2005. FOAM – Framework for Ontology Alignment and Mapping – Results of the ontology alignment evaluation initiative. In Proceedings of the Integrating Ontologies IO'05, Banff, Canada.Google Scholar
Ehrig, M., Staab, S., Sure, Y. 2005. Bootstrapping ontology alignment methods with APFEL. In Proceedings of the International Semantic Web Conference ISWC'05. Springer-Verlag, 186–200.Google Scholar
Euzenat, J., Shvaiko, P. 2007. Ontology Matching. Springer-Verlag.Google Scholar
Falconer, D., Noy, N. 2007. Ontology Mapping – an user survey. In Proceedings of The Second International Workshop on Ontology Matching ISWC/ASWC'07, Busan, Korea, 49–60.Google Scholar
Fasli, M. 2007. On agent technology for e-commerce: trust, security and legal issues. The Knowledge Engineering Review 22(1), 335.CrossRefGoogle Scholar
Forbus, K., Gentner, D., Law, K. 1995. MAC/FAC: a model of similarity-based retrieval. Cognitive Science 19(2), 141205.Google Scholar
Forrest, S. 1997. Genetic Algorithms. In The Computer Science and Engineering Handbook, Tuker, A. B. (ed.). CRC Press, 557–571.Google Scholar
Giunchiglia, F., Yatskevich, M., Avesani, P., Shvaiko, P. 2009. A large dataset for the evaluation of ontology matching. The Knowledge Engineering Review 24(2), 137157.CrossRefGoogle Scholar
Gracia, J., Mena, E. 2008. Web-based measure of semantic relatedness. In Proceedings of the Web Information Systems Engineering WISE'08. Springer-Verlag, 136–150.Google Scholar
Hai Do, H., Rahm, E. 2002. COMA – a system for flexible combination of schema matching approaches. In Proceedings of Very Large Databases VLDB'02, Hong Kong, China, 610–621.Google Scholar
He, B., Chen-Chuan Chang, K. 2005. Making holistic schema matching robust: an ensemble approach. In Proceedings of the Knowledge Discovery and Data Mining KDD'05, Springer-Verlag, 429–438.Google Scholar
Huang, J., Dang, J., Vidal, J. M., Huhns, M. 2007. Ontology matching using an artificial neural network to learn weights. In Proceedings of the IJCAI Workshop on Semantic Web for Collaborative Knowledge, Hyderabad, India.Google Scholar
Ji, Q., Liu, W., Qi, G., Bell, D. 2006. LCS: a linguistic combination system for ontology matching. In Proceedings of the International Conference on Knowledge Science, Engineering and Management KSEM'06, Guilin, China, 176–189.Google Scholar
Jordan, M., Bishop, C. 1997. Neural networks. In The Computer Science and Engineering Handbook, Tucker, A. B. (ed.). CRC Press, 536556.Google Scholar
Kalfoglou, Y., Schorlemmer, M. 2003a. IF-Map: an ontology-mapping method based on information-flow theory. Journal of Data Semantics 1, 98127.CrossRefGoogle Scholar
Kalfoglou, Y., Schorlemmer, M. 2003b. Ontology mapping: the state of the art. The Knowledge Engineering Review 18(1), 131.CrossRefGoogle Scholar
Kiefer, C., Bernstein, A., Stocker, M. 2007. The fundamentals of iSPARQL: a virtual triple approach for similarity-based semantic web tasks. In Proceedings of the International/Asian Semantic Web Conference ISWC/ASWC'07. Springer-Verlag, 295–309.Google Scholar
Lambrix, P., Tan, H. 2007. A tool for evaluating ontology alignment strategies. Journal on Data Semantics 8, 182202.Google Scholar
Langley, P. 1994. Elements of Machine Learning. Morgan Kaufmann, ISBN 1-55860-301-8.Google Scholar
Lee, Y., Sayyadian, M., Doan, A., Rosenthal, A. 2007. eTuner: tuning schema matching software using synthetic scenarios. The International Journal on Very Large Data Bases 16(1), 97122.CrossRefGoogle Scholar
Levenshtein, V. 1966. Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics-Doklady 10, 707710.Google Scholar
Li, J., Tang, J., Li, Y., Luo, Q. 2009. RiMOM: a dynamic multistrategy ontology alignment framework. IEEE Transactions on Knowledge and Data Engineering 21(8), 12181232.Google Scholar
Li, W. S., Clifton, C. 2000. SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks. Data and Knowledge Engineering 33(1), 4984.CrossRefGoogle Scholar
Lomax, J. 2005. Get ready to GO! A biologist's guide to the Gene Ontology. Briefings in Bioinformatics 6(3), 298304.CrossRefGoogle Scholar
Mao, M., Peng, Y., Spring, M. 2008. Neural network based constraint satisfaction in ontology mapping. In Proceedings of the Conference on Artificial Intelligence AAAI ‘08. AAAI Press, 1207–1212.Google Scholar
Martinez-Gil, J., Aldana-Montes, J. F. 2011. Evaluation of two heuristic approaches to solve the ontology meta-matching problem. Knowledge and Information Systems 26(2), 225247.CrossRefGoogle Scholar
Martinez-Gil, J., Alba, E., Aldana-Montes, J. F. 2008. Optimizing ontology alignments by using genetic algorithms. In Proceedings of the NatuReS, CEUR-Proceedings.Google Scholar
Martinez-Gil, J., Alba, E., Aldana-Montes, J. F. 2010. Statistical Study about Existing OWL Ontologies from a Significant Sample as Previous Step for their Alignment. In Proceedings of the Conference on Complex, Intelligent and Software Intensive Systems CISIS, IEEE Computer Society, 980–985.Google Scholar
Nebro, A. J., Luna, F., Alba, E., Dorronsoro, B., Durillo, J. J., Beham, A. 2008. AbYSS: adapting scatter search to multiobjective optimization. IEEE Transactions on Evolutionary Computation 12(4), 439457.CrossRefGoogle Scholar
Noy, N. 2004. Semantic integration: a survey of ontology-based approaches. ACM Sigmod Record 33(4), 6570.CrossRefGoogle Scholar
Oberle, D., Ankolekar, A., Hitzler, P., Cimiano, P., Sintek, M., Kiesel, M., Mougouie, B., Baumann, S., Vembu, S., Romanelli, M. 2007. DOLCE ergo SUMO: on foundational and domain models in the SmartWeb Integrated Ontology (SWIntO). Journal of Web Semantics 5(3), 156174.CrossRefGoogle Scholar
Pan, F., Hobbs, J. 2005. Temporal aggregates in OWL-Time. In Proceedings of the Florida Artificial Intelligence Research Society FLAIRS'05, Clearwater Beach, FL, USA, 560–565.Google Scholar
Pedersen, T., Patwardhan, D., Michelizzi, J. 2004. WordNet::Similarity – measuring the relatedness of concepts. In Proceedings of the Ameriacan Association for Artificial Intelligence AAAI'04. AAAI Press, 1024–1025.Google Scholar
Rahm, E., Bernstein, P. 2001. A survey of approaches to automatic schema matching. The International Journal on Very Large Data Bases 10(4), 334350.CrossRefGoogle Scholar
Roitman, H., Gal, A. 2006. OntoBuilder: fully automatic extraction and consolidation of ontologies from web sources using sequence semantics. In Proceedings of the Extending Data Base Technology EDBT Workshops, Springer-Verlag, 573–576.Google Scholar
Schulz, S., Suntisrivaraporn, B., Baader, F. 2007. SNOMED CT's problem list: ontologists’ and logicians’ therapy suggestions. MedInfo, Brisbane, Australia, 802–806.Google Scholar
Shvaiko, P., Euzenat, J. 2005. A survey of schema-based matching approaches. Journal on Data Semantics 4, 146171.Google Scholar
Shvaiko, P., Euzenat, J. 2008. Ten challenges for ontology matching. In Proceedings of the On The Move Conferences OTM'08. Springer-Verlag, 2, 1164–1182.Google Scholar
Svab, O., Svtek, V. 2006. Ontology mapping enhanced using bayesian networks. In Proceedings of the Ontology Matching, Athens, GA, USA.Google Scholar
Wang, J., Ding, J., Jiang, C. 2006. GAOM: Genetic Algorithm based Ontology Matching. In Proceedings of the Asia-Pacific Services Computing Conference APSCC'06, IEEE Computer Society.CrossRefGoogle Scholar
Widdows, D. 2004. Geometry and Meaning. The University of Chicago Press.Google Scholar
Ziegler, P., Kiefer, C., Sturm, C., Dittrich, K., Bernstein, A. 2006. Detecting similarities in ontologies with the SOQA-SimPack toolkit. In Proceedings of the Extending Data Base Technology conference EDBT'06. Springer-Verlag, 59–76.Google Scholar