Evaluation of Candidate Pair Generation Strategies in Entity Matching

Entity Matching (EM) involves identifying and linking the given entities from various sources that pertain to identical real-world entities, serving as a fundamental element in data integration tasks. Such matching is assumed to play a pivotal role in enhancing the accuracy and reliability of downstream tasks in data analytics. Typically, the EM procedure comprises two essential stages: blocking and matching. This study focuses on the blocking phase, particularly the operations of candidate pair generation. Thus, the focal point of this study resides in the exploration of different techniques for generating candidate pairings from the sources during the blocking phase. The proposed work is evaluated by experiment on the benchmark datasets, which are DBLP-ACM and Amazon-GoogleProducts.