Machamp: A Generalized Entity Matching Benchmark

TL;DR

Machamp introduces a comprehensive benchmark for generalized entity matching across diverse data structures, enabling evaluation of matching techniques beyond traditional structured table scenarios, thus advancing research in real-world data integration.

Contribution

The paper presents Machamp, a new benchmark with seven diverse tasks for generalized entity matching across various data structures, filling a gap in existing EM evaluation methods.

Findings

Existing EM benchmarks are limited to structured tables.

Machamp enables evaluation across structured, semi-structured, and unstructured data.

Popular EM approaches are evaluated on Machamp, revealing their strengths and limitations.

Abstract

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.