  Entity resolution (ER) is the problem of identifying and merging records that
refer to the same real-world entity. In many scenarios, raw records are stored
under heterogeneous environment. Specifically, the schemas of records may
differ from each other. To leverage such records better, most existing work
assume that schema matching and data exchange have been done to convert records
under different schemas to those under a predefined schema. However, we observe
that schema matching would lose information in some cases, which could be
useful or even crucial to ER.
  To leverage sufficient information from heterogeneous sources, in this paper,
we address several challenges of ER on heterogeneous records and show that none
of existing similarity metrics or their transformations could be applied to
find similar records under heterogeneous settings. Motivated by this, we design
the similarity function and propose a novel framework to iteratively find
records which refer to the same entity. Regarding efficiency, we build an index
to generate candidates and accelerate similarity computation. Evaluations on
real-world datasets show the effectiveness and efficiency of our methods.
