Amazon-Google, Augmented Version, Fixed Splits
- Primpeli, Anna (University of Mannheim (Germany))
- Bizer, Christian (University of Mannheim (Germany))
Entity Matching is the task of determining which records from different data sources describe the same real-world entity. It is an important task for data integration and has been the focus of many research works. A large number of entity matching/record linkage tasks has been made available for evaluating entity matching methods. However, the lack of fixed development and test splits as well as correspondence sets including both matching and non-matching record pairs hinders the reproducibility and comparability of benchmark experiments. In an effort to enhance the reproducibility and comparability of the experiments, we complement existing entity matching benchmark tasks with fixed sets of non-matching pairs as well as fixed development and test splits.
An augmented version of the amazon-google products dataset for benchmarking entity matching/record linkage methods found at:
The augmented version adds a fixed set of non-matching pairs to the original dataset. In addition, fixed splits for training, validation and testing as well as their corresponding feature vectors are provided. The feature vectors are built using data type specific similarity metrics.
The dataset contains 1,363 records describing products deriving from amazon which are matched against 3,226 product records from google. The gold standards have manual annotations for 1,298 matching and 6,306 non-matching pairs. The total number of attributes used to decribe the product records are 4 while the attribute density is 0.75.
The augmented dataset enhances the reproducibility of matching methods and the comparability of matching results.
The dataset is part of the CompERBench repository which provides 21 complete benchmark tasks for entity matching for public download:
Is version of
Primpeli, Anna, and Christian Bizer. “Profiling Entity Matching Benchmark Tasks.” Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York, NY, USA: ACM, October 19, 2020. https://doi.org/10.1145/3340531.3412781.
- ID: 10.1145/3340531.3412781 (DOI)
Update Metadata: 2020-11-23 | Issue Number: 1 | Registration Date: 2020-11-23