AdaMEL models the attribute importance that is used to match entities through an attribute-level self-attention mechanism, and leverages the massive unlabeled data from new data sources through domain adaptation to make it generic and data-source agnostic.
We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions.
We evaluate CorDEL with extensive experiments conducted on both public benchmark datasets and a real-world dataset.
Ranked #6 on Entity Resolution on Amazon-Google
However, this task is challenging as the variational attributes are often present as a part of unstructured text and are domain dependent.
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity.
To address the problem, this paper proposes an efficient sampling and evaluation framework, which aims to provide quality accuracy evaluation with strong statistical guarantee while minimizing human efforts.
Knowledge graphs have emerged as an important model for studying complex multi-relational data.