We propose a deep Siamese neural network that learns a similarity measure tailored to a dataset, eliminating manual feature engineering. We also show that knowledge transfer …
Our system uses automatically discovered MDs, dataset features, and known gold standards to train a model that selects MDs as duplicate detection rules.
We propose the first workflow that systematically integrates data preparation operations before duplicate detection, improving AUC-PR by up to 19%.
In this paper, we study the problem of matching records that contain address information, including attributes such as Street-address and City. To facilitate this matching process …
We introduce SearchER, a method for progressively performing search-driven Entity Resolution on user-indicated entities defined by keyword queries.
Matching financial entities (FEs) is important for many private and governmental organizations. In this paper we describe the problem of matching such FEs across three datasets: …
Collaborative recommenders rely on the assumption that similar users may exhibit similar tastes while content-based ones favour items that found to be similar with the items a user …