Abstract
In this paper we analyze the problem of schema matching, explain why it is such a "tough" problem and suggest directions for handling it effectively. In particular, we present the monotonicity principle and see how it leads to the use of top-K mappings rather than a single mapping.
- A. Anaby-Tavor. Enhancing the formal similarity based matching model. Master's thesis, Technion-Israel Institute of Technology, May 2003.]]Google Scholar
- M. Benerecetti, P. Bouquet, and S. Zanobini. Soundness of schema matching methods. In Proceedings of ESWC 2005, pages 211--225, 2005.]] Google ScholarDigital Library
- T. Berners-Lee, J. Hendler, and O. Lassila. The semantic Web. Scientific American, May 2001.]]Google ScholarCross Ref
- P. A. Bernstein and S. Melnik. Meta data management. In Proceedings of the IEEE CS International Conference on Data Engineering. IEEE Computer Society, 2004.]] Google ScholarDigital Library
- A. Bilke and F. Naumann. Schema matching using duplicates. In Proceedings of the IEEE CS International Conference on Data Engineering, pages 69--80, 2005.]] Google ScholarDigital Library
- B. Convent. Unsolvable problems related to the view integration approach. In Proceedings of the International Conference on Database Theory (ICDT), Rome, Italy, September 1986. In Computer Science, Vol. 243, G. Goos and J. Hartmanis, Eds. Springer-Verlag, New York, pp. 141--156.]] Google ScholarDigital Library
- H. Do, S. Melnik, and E. Rahm. Comparison of schema matching evaluations. In Proceedings of the 2nd Int. Workshop on Web Databases (German Informatics Society), 2002., 2002.]] Google ScholarDigital Library
- H. H. Do and E. Rahm. COMA - a system for flexible combination of schema matching approaches. In Proceedings of the International conference on very Large Data Bases (VLDB), pages 610--621, 2002.]]Google ScholarDigital Library
- A. Gal. On the cardinality of schema matching. In IFIP WG 2.12 and WG 12.4 International Workshop on Web Semantics (SWWS), pages 947--956, 2005.]] Google ScholarDigital Library
- A. Gal. Managing uncertainty in schema matching with top-k schema mappings. Journal of Data Semantics, 6:90-114, 2006.]] Google ScholarDigital Library
- A. Gal, A. Anaby-Tavor, A. Trombetta, and D. Montesi. A framework for modeling and evaluating automatic semantic reconciliation. VLDB Journal, 14(1):50--67, 2005.]] Google ScholarDigital Library
- B. He and K. C.-C. Chang. Making holistic schema matching robust: an ensemble approach. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, August 21--24, 2005, pages 429--438, 2005.]] Google ScholarDigital Library
- R. Hull. Managing semantic heterogeneity in databases: A theoretical perspective. In Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), pages 51--61. ACM Press, 1997.]] Google ScholarDigital Library
- M. Lenzerini. Data integration is harder than you thought. In C. Batini, F. Giunchiglia, P. Giorgini, and M. Mecella, editors, Cooperative Information Systems, 9th International Conference, CoopIS 2001, Trento, Italy, September 5--7, 2001, Proceedings, volume 2172 of Lecture Notes in Computer Science, pages 22--26. Springer, 2001.]] Google ScholarDigital Library
- J. Madhavan, P. A. Bernstein, P. Domingos, and A. Y. Halevy. Representing and reasoning about mappings between domain models. In Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI), pages 80--86, 2002.]] Google ScholarDigital Library
- M. Magnani, N. Rizopoulos, P. McBrien, and D. Montesi. Schema integration based on uncertain semantic mappings. In ER, pages 31--46, 2005.]] Google ScholarDigital Library
- S. Melnik. Generic Model Management: Concepts and Algorithms. Springer-Verlag, 2004.]] Google ScholarDigital Library
- S. Melnik, H. Garcia-Molina, and E. Rahm. Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In Proceedings of the IEEE CS International Conference on Data Engineering, pages 117--140, 2002.]] Google ScholarDigital Library
- R. J. Miller, L. M. Haas, and M. A. Hernández. Schema mapping as query discovery. In A. El Abbadi, M. L. Brodie, S. Chakravarthy, U. Dayal, N. Kamel, G. Schlageter, and K.-Y. Whang, editors, Proceedings of the International conference on very Large Data Bases (VLDB), pages 77--88. Morgan Kaufmann, 2000.]] Google ScholarDigital Library
- K. G. Murty. An algorithm for ranking all the assignments in order of increasing cost. Operations Research, 16:682--687, 1968.]]Google ScholarDigital Library
- E. Rahm and P. A. Bernstein. A survey of approaches to automatic schema matching. VLDB Journal, 10(4):334--350, 2001.]] Google ScholarDigital Library
- M. Sayyadian, Y. Lee, A. Doan, and A. Rosenthal. Tuning schema matching software using synthetic scenarios. In Proceedings of the International conference on very Large Data Bases (VLDB), pages 994--1005, 2005.]] Google ScholarDigital Library
- P. Shvaiko and J. Euzenat. A survey of schema-based matching approaches. Journal of Data Semantics, 4:146 -- 171, December 2005.]] Google ScholarDigital Library
- W. Su, J. Wang, and F. Lochovsky. Aholistic schema matching for web query interfaces. In Advances in Database Technology - EDBT 2006, 10th International Conference on Extending Database Technology, Munich, Germany, March 26--31, 2006, Proceedings, pages 77--94, 2006.]] Google ScholarDigital Library
Index Terms
- Why is schema matching tough and what can we do about it?
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