document: Discovering complex matchings across web query interfaces : a correlation mining approach To enable information integration , schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources . While complex matchings are common , because of their far more complex search space , most existing techniques focus on simple 1:1 matchings . To tackle this challenge , this paper takes a conceptually novel approach by viewing schema matching as correlation mining , for our task of matching Web query interfaces to integrate the myriad databases on the Internet . On this `` deep Web , '' query interfaces generally form complex matchings between attribute groups ( e.g. , ( author ) corresponds to ( first name , last name ) in the Books domain ) . We observe that the co-occurrences patterns across query interfaces often reveal such complex semantic relationships : grouping attributes ( e.g. , ( first name , last name ) ) tend to be co-present in query interfaces and thus positively correlated . In contrast , synonym attributes are negatively correlated because they rarely co-occur . This insight enables us to discover complex matchings by a correlation mining approach . In particular , we develop the DCM framework , which consists of data preparation , dual mining of positive and negative correlations , and finally matching selection . Unlike previous correlation mining algorithms , which mainly focus on finding strong positive correlations , our algorithm cares both positive and negative correlations , especially the subtlety of negative correlations , due to its special importance in schema matching . This leads to the introduction of a new correlation measure , $ H$ - measure , distinct from those proposed in previous work . We evaluate our approach extensively and the results show good accuracy for discovering complex matchings .
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