A Framework to Combine Multiple Matchers for Pair-Wise Schema Matching

There are many matching tools (or matchers) have been develop to generate correspondences of elements between two schema. However, the performances of those matchers are highly dependant on the domains they are applied. One tool may achieve best performance in a specific domain but worst when applied in other ones. In this work we propose a combination technique, which enhances mapping quality by merging several mappings. We rely on the well-known Stable Marriage (SM) approach to perform the suitable selection between multiple matching results. In order to reduce complexity and increase the accuracy of SM algorithm, we suggest to combine it with Hyperlink-Induced Topic Search (HITS) algorithm, which can reasonably filter out good candidates for matching selection. We show empirically that the combined solution yields better result than individual matchers in various domains in terms of precision and recall.

[1]  David Manlove,et al.  Hard variants of stable marriage , 2002, Theor. Comput. Sci..

[2]  Cosmin Stroe,et al.  AgreementMaker: Efficient Matching for Large Real-World Schemas and Ontologies , 2009, Proc. VLDB Endow..

[3]  ZVI GALIL,et al.  Efficient algorithms for finding maximum matching in graphs , 1986, CSUR.

[4]  Alon Y. Halevy,et al.  Semantic Integration Research in the Database Community : A Brief Survey , 2005 .

[5]  Erhard Rahm,et al.  Schema and ontology matching with COMA++ , 2005, SIGMOD '05.

[6]  K. Iwama,et al.  A Survey of the Stable Marriage Problem and Its Variants , 2008, International Conference on Informatics Education and Research for Knowledge-Circulating Society (icks 2008).

[7]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[8]  Zohra Bellahsene,et al.  Improving quality and performance of schema matching in large scale , 2008, Ingénierie des Systèmes d Inf..

[9]  Erhard Rahm,et al.  COMA - A System for Flexible Combination of Schema Matching Approaches , 2002, VLDB.

[10]  Erhard Rahm,et al.  Generic schema matching, ten years later , 2011, Proc. VLDB Endow..

[11]  J. Euzenat,et al.  Ontology Matching , 2007, Springer Berlin Heidelberg.

[12]  Avigdor Gal,et al.  OntoBuilder: fully automatic extraction and consolidation of ontologies from Web sources , 2004, Proceedings. 20th International Conference on Data Engineering.

[13]  Erhard Rahm,et al.  A survey of approaches to automatic schema matching , 2001, The VLDB Journal.

[14]  Yi Li,et al.  RiMOM: A Dynamic Multistrategy Ontology Alignment Framework , 2009, IEEE Transactions on Knowledge and Data Engineering.

[15]  Mansur R. Kabuka,et al.  Ontology matching with semantic verification , 2009, J. Web Semant..

[16]  Jayant Madhavan,et al.  OpenII: an open source information integration toolkit , 2010, SIGMOD Conference.

[17]  Avigdor Gal,et al.  On the Stable Marriage of Maximum Weight Royal Couples , 2007 .

[18]  Yuzhong Qu,et al.  Falcon-AO: A practical ontology matching system , 2008, J. Web Semant..

[19]  Yildiray Kabak,et al.  A survey and analysis of electronic business document standards , 2010, CSUR.

[20]  Erhard Rahm,et al.  Generic Schema Matching with Cupid , 2001, VLDB.

[21]  Zohra Bellahsene,et al.  BMatch: a Semantically Context-based Tool Enhanced by an Indexing Structure to Accelerate Schema Matching , 2007, BDA.

[22]  L. S. Shapley,et al.  College Admissions and the Stability of Marriage , 2013, Am. Math. Mon..

[23]  Eric Peukert,et al.  AMC - A framework for modelling and comparing matching systems as matching processes , 2011, 2011 IEEE 27th International Conference on Data Engineering.