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- W4385270265 abstract "Entity Resolution is the task of identifying pairs of entity profiles that represent the same real-world object. To avoid checking a quadratic number of entity pairs, various filtering techniques have been proposed that fall into two main categories: (i) blocking workflows group together entity profiles with identical or similar signatures, and (ii) nearest-neighbor methods convert all entity profiles into vectors and identify the closest ones to every query entity. Unfortunately, the main techniques from these two categories have rarely been compared in the literature and, thus, their relative performance is unknown. We perform the first systematic experimental study that investigates the relative performance of the main representatives per category over numerous established datasets. Comparing techniques from different categories turns out to be a non-trivial task due to the various configuration parameters that are hard to fine-tune, but have a significant impact on performance. We consider a plethora of parameter configurations, optimizing each technique with respect to recall and precision targets. Both schema-agnostic and schema-based settings are evaluated. The experimental results provide novel insights into the effectiveness, the time efficiency and the scalability of the considered techniques." @default.
- W4385270265 created "2023-07-27" @default.
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- W4385270265 date "2023-04-01" @default.
- W4385270265 modified "2023-10-01" @default.
- W4385270265 title "Benchmarking Filtering Techniques for Entity Resolution" @default.
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- W4385270265 doi "https://doi.org/10.1109/icde55515.2023.00389" @default.
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