Automatic complex schema matching across Web query interfaces

To enable information integration, schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources. While complex matchings are common, becaus...

[1]  Kevin Chen-Chuan Chang,et al.  Statistical schema matching across web query interfaces , 2003, SIGMOD '03.

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

[3]  Luis Gravano,et al.  Probe, count, and classify: categorizing hidden web databases , 2001, SIGMOD '01.

[4]  H. Young.  Condorcet's Theory of Voting , 1988, American Political Science Review.

[5]  Clement T. Yu,et al.  An interactive clustering-based approach to integrating source query interfaces on the deep Web , 2004, SIGMOD '04.

[6]  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.

[7]  S. Shapiro,et al.  Mathematics without Numbers , 1993 .

[8]  Kevin Chen-Chuan Chang,et al.  Understanding Web query interfaces: best-effort parsing with hidden syntax , 2004, SIGMOD '04.

[9]  Mitesh Patel,et al.  Structured databases on the web: observations and implications , 2004, SGMD.

[10]  Kevin Chen-Chuan Chang,et al.  Light-weight Domain-based Form Assistant: Querying Web Databases On the Fly , 2005, VLDB.

[11]  Kevin Chen-Chuan Chang,et al.  Toward Large Scale Integration: Building a MetaQuerier over Databases on the Web , 2005, CIDR.

[12]  R. Graham,et al.  Spearman's Footrule as a Measure of Disarray , 1977 .

[13]  Rajeev Motwani,et al.  Robust and efficient fuzzy match for online data cleaning , 2003, SIGMOD '03.

[14]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[15]  Kevin Chen-Chuan Chang,et al.  Making holistic schema matching robust: an ensemble approach , 2005, KDD '05.

[16]  P.-C.-F. Daunou,et al.  Mémoire sur les élections au scrutin , 1803 .

[17]  Jiawei Han,et al.  CoMine: efficient mining of correlated patterns , 2003, Third IEEE International Conference on Data Mining.

[18]  Martin Bergman,et al.  The deep web:surfacing the hidden value , 2000 .

[19]  Ronald Fagin,et al.  Efficient similarity search and classification via rank aggregation , 2003, SIGMOD '03.

[20]  Wei-Ying Ma,et al.  Instance-based Schema Matching for Web Databases by Domain-specific Query Probing , 2004, VLDB.

[21]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

[22]  Jaideep Srivastava,et al.  Selecting the right interestingness measure for association patterns , 2002, KDD.

[23]  Leonard J. Seligman,et al.  Bulletin of the Technical Committee on Data Engineering September 2002 , 2002 .

[24]  H. P. Young,et al.  An axiomatization of Borda's rule , 1974 .

[25]  Pedro M. Domingos,et al.  iMAP: discovering complex semantic matches between database schemas , 2004, SIGMOD '04.

[26]  Moni Naor,et al.  Rank aggregation methods for the Web , 2001, WWW '01.

[27]  T. Teichmann,et al.  An introduction to mathematical statistics , 1960 .

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

[29]  L. A. Goodman,et al.  Measures of association for cross classifications , 1979 .

[30]  Pedro M. Domingos,et al.  Reconciling schemas of disparate data sources: a machine-learning approach , 2001, SIGMOD '01.

[31]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[32]  Jiawei Han,et al.  Discovering complex matchings across web query interfaces: a correlation mining approach , 2004, KDD.