Abstract
Schema matching has been one of the basic tasks in almost every data intensive distributed applications such as enterprize information integration, collaborating web services, web catalogue integration, and schema based point to point database systems and so on. Typical schema matchers perform manually and use a set of matching algorithms with a composition function by using them in an arbitrary manner which results in wasteful computations and needs manual specification for different domains. Recently, there has been some schema matching strategy proposed with partial or full automation. Such a schema matching strategy is OntoMatch. In this paper, we propose an element level automated linguistic based schema matching strategy motivated by the concept of OntoMatch, with more powerful matching algorithms and definite property construction for matcher selection that produces better output. Experimental result is also provided to support the claim of the improvement.
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References
JDOM, http://www.jdom.org
UIUC Web Repository, http://metaquerier.cs.uiuc.edu/repository/datasets/bamm/index.html
WordNet, http://wordnet.princeton.edu/
Jaro, M.A.: Probabilistic linkage of large public health data files. Stat Med. 14(5-7), 491–498 (1999)
Bhattacharjee, A., Jamil, H.M.: OntoMatch: A monotonically improving schema matching system for autonomous data integration. In: IEEE IRI, Las Vegas, NV (2009)
Do, H.H., Rahm, E.: COMA - A system for flexible combination of schema matching approaches. In: VLDB, Hong Kong, China, pp. 610–621 (2002)
Hosain, S., Jmail, H.: An algebraic language for semantic data integration on the hidden web. In: Proceedings of the 3rd IEEE ICSC 2009, Berkeley, California, pp. 237–244 (2009)
Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with Cupid. In: VLDB, Italy, pp. 49–58 (2001)
Monge, A., Elkan, C.: The field matching problem: Algorithms and applications. In: 2nd International Conference on KDDM, pp. 267–270 (1996)
Ristad, E.S., Yianilos, P.N.: Learning string edit distance. IEEE Transactions on Pattern Recognition and Machine Intelligence 20(5), 522–532 (1998)
Winkler, W.E.: The state of record linkage and current research problems. Tech. rep., Statistical Research Division, U.S. Census Bureau (1999)
Zobel, J.: Phonetic string matching: Lessons from information retrieval. In: SIGIR 1996, pp. 166–172 (1996)
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Chakraborty, R., Ahmed, F., Hosain, S. (2013). CASM: Coherent Automated Schema Matcher. In: Gaol, F. (eds) Recent Progress in Data Engineering and Internet Technology. Lecture Notes in Electrical Engineering, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28807-4_31
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DOI: https://doi.org/10.1007/978-3-642-28807-4_31
Publisher Name: Springer, Berlin, Heidelberg
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