Abstract Entity resolution (ER) is the task of finding records that refer to the same real-world entities. A common scenario, which we refer to as Clean-Clean ER, is to resolve records across two clean sources (i.e., they are duplicate-free and contain one record per entity). Matching algorithms for Clean-Clean ER yield bipartite graphs, which are further processed by clustering algorithms to produce the end result. In this paper, we perform an extensive empirical evaluation of eight bipartite graph matching algorithms that take as input a bipartite similarity graph and provide as output a set of matched records. We consider a wide range of matching algorithms, including algorithms that have not previously been applied to ER, or have been evaluated only in other ER settings. We assess the relative performance of these algorithms with respect to accuracy and time efficiency over ten established real-world data sets, from which we generated over 700 different similarity graphs. Our results provide insights into the relative performance of these algorithms and guidelines for choosing the best one, depending on the data at hand.

JedAI is an open-source toolkit that allows for building and benchmarking thousands of schema-agnostic Entity Resolution (ER) pipelines through a non-learning, blocking-based end-to-end workflow. In this paper, we present its latest release, JedAI3 , which conveys two new end-to-end workflows: one for budgetagnostic ER that is based on similarity joins, and one for budgetaware (i.e., progressive) ER. This version also adds support for pre-trained word or character embeddings and connects JedAI to the Python data analysis ecosystem. Overall, these enhancements provide JedAI with features offered by no other ER tool, especially in the schema- and domain-agnostic context.

JedAI is an Entity Resolution toolkit that can be used in three ways: (i) as an open-source library that combines state-of-the-art methods into a plethora of end-to-end workflows, (ii) as a user-friendly desktop application with a wizardlike interface that provides complex, out-of-the-box solutions even to lay users, and (iii) as a workbench for comparing the performance of numerous workflows over both structured and semi-structured data. Here, we present its significant upgrade, JedAI 2.0, which enhances the original version in three important respects: (i) time efficiency , as the running time has been drastically reduced with the use of high performance data structures and multi-core processing, (ii) effectiveness , since we enriched its library with more established methods, a new layer that exploits loose schema binding as well as the automatic, data-driven configuration of individual methods or entire workflows, and (iii) usability , as the GUI now enables users to manually configure any method based on concrete guidelines, to store the matching results into any of the supported data formats and to visually explore both input and output data.