چکیده (انگلیسی):

Holistic schema matching is the process of carrying off several
number of schemas as an input and outputs the correspondences among them.
Treating large number of schemas may consume longer time with poor
quality. Therefore, several clustering approaches have been proposed in order
to reduce the search space by partitioning the data into smaller portions which
can facilitate the matching process. However, there is still a demand for
improving the partitioning mechanism by avoiding the random initial
solutions (centroids) re-sulted from the clustering process. Such random
solutions have a significant impact on the matching results. This study aims
to integrate correlation clustering and agglomerative hierarchical clustering
toward improving the effectiveness of holistic schema matching. The
proposed integrated method avoids the random initial so-lutions and the
predefined number of centroids. Several preprocessing steps have been
performed with using auxiliary information (domain dictionary). The
experiments have been carried out on Airfare, Auto and Book datasets from
UIUC Web Integration Repository. The proposed method has been compared
with K-means and K-medoids clustering methods. As a results the proposed
method has outperformed K-means and K-medoids by achieving 0.9, 0.93
and 0.9 of accuracy for Airfare, Auto and Book respectively.