  Entity Matching (EM) refers to the problem of determining whether two
different data representations refer to the same real-world entity. It has been
a long-standing interest of the data management community and many efforts have
been paid in creating benchmark tasks as well as in developing advanced
matching techniques. However, existing benchmark tasks for EM are limited to
the case where the two data collections of entities are structured tables with
the same schema. Meanwhile, the data collections for matching could be
structured, semi-structured, or unstructured in real-world scenarios of data
science. In this paper, we come up with a new research problem -- Generalized
Entity Matching to satisfy this requirement and create a benchmark Machamp for
it. Machamp consists of seven tasks having diverse characteristics and thus
provides good coverage of use cases in real applications. We summarize existing
EM benchmark tasks for structured tables and conduct a series of processing and
cleaning efforts to transform them into matching tasks between tables with
different structures. Based on that, we further conduct comprehensive profiling
of the proposed benchmark tasks and evaluate popular entity matching approaches
on them. With the help of Machamp, it is the first time that researchers can
evaluate EM techniques between data collections with different structures.
