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Found 5 papers, 4 papers with code
Sparkly: A Simple yet Surprisingly Strong TF/IDF Blocker for Entity Matching
We develop Sparkly, which uses Lucene to perform top-k tf/idf blocking in a distributed share-nothing fashion on a Spark cluster.
Ranked #2 on
Blocking
on Amazon-Google
Deep learning for blocking in entity matching: a design space exploration
In this paper, we develop the DeepBlocker framework that significantly advances the state of the art in applying DL to blocking for EM.
Ranked #5 on
Blocking
on Abt-Buy
Deep Entity Matching with Pre-Trained Language Models
Our experiments show that a straightforward application of language models such as BERT, DistilBERT, or RoBERTa pre-trained on large text corpora already significantly improves the matching quality and outperforms previous state-of-the-art (SOTA), by up to 29% of F1 score on benchmark datasets.
Ranked #2 on
Entity Resolution
on WDC Watches-xlarge
Deep Learning for Entity Matching: A Design Space Exploration
Entity matching (EM) finds data instances that refer to the same real-world entity.
Ranked #8 on
Entity Resolution
on Amazon-Google
Toward a System Building Agenda for Data Integration
They provide tools to address the "pain points" of the steps, and tools are built on top of the Python data science and Big Data ecosystem (PyData).
Databases
