Affiliation:
1. University of Illinois at Urbana-Champaign, Urbana, IL
Abstract
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 article 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 preprocessing
,
dual mining
of positive and negative correlations, and finally
matching construction
. We evaluate the DCM framework on manually extracted interfaces and the results show good accuracy for discovering complex matchings. Further, to automate the entire matching process, we incorporate automatic techniques for interface extraction. Executing the DCM framework on automatically extracted interfaces, we find that the inevitable errors in automatic interface extraction may significantly affect the matching result. To make the DCM framework robust against such “noisy” schemas, we integrate it with a novel “ensemble” approach, which creates an ensemble of DCM matchers, by randomizing the schema data into many
trials
and aggregating their ranked results by taking majority voting. As a principled basis, we provide analytic justification of the robustness of the ensemble approach. Empirically, our experiments show that the “ensemblization” indeed significantly boosts the matching accuracy, over automatically extracted and thus noisy schema data. By employing the DCM framework with the ensemble approach, we thus complete an automatic process of matchings Web query interfaces.
Publisher
Association for Computing Machinery (ACM)
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