Name
Abstract | ||
|---|---|---|
Entity Resolution (ER) is the task of identifying different representations of the same real-world object. To achieve scalability and the desired level of quality, the typical ER pipeline includes multiple steps that may involve low-level coding and extensive human labor. We present SystemER, a tool for learning explainable ER models that reduces the human labor all throughout the stages of the ER pipeline. SystemER achieves explainability by learning rules that not only perform a given ER task but are human-comprehensible; this provides transparency into the learning process, and further enables verification and customization of the learned model by the domain experts. By leveraging a human in the loop and active learning, SystemER also ensures that a small number of labeled examples is sufficient to learn high-quality ER models. SystemER is a full-fledged tool that includes an easy to use interface, support for both flat files and semi-structured data, and scale-out capabilities by distributing computation via Apache Spark.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.14778/3352063.3352068 | PVLDB |
Field | DocType | Volume |
Data mining,Name resolution,Computer science,Human-in-the-loop | Journal | 12 |
Issue | ISSN | Citations |
12 | 2150-8097 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Qian | 1 | 8 | 2.81 |
| Ling-ling Yan | 2 | 1273 | 70.78 |
| Prithviraj Sen | 3 | 837 | 38.24 |