End-to-End Entity Resolution for Big Data: A Survey
arXiv:1905.06397
Abstract
One of the most important tasks for improving data quality and the reliability of data analytics results is Entity Resolution (ER). ER aims to identify different descriptions that refer to the same real-world entity, and remains a challenging problem. While previous works have studied specific aspects of ER (and mostly in traditional settings), in this survey, we provide for the first time an end-to-end view of modern ER workflows, and of the novel aspects of entity indexing and matching methods in order to cope with more than one of the Big Data characteristics simultaneously. We present the basic concepts, processing steps and execution strategies that have been proposed by different communities, i.e., database, semantic Web and machine learning, in order to cope with the loose structuredness, extreme diversity, high speed and large scale of entity descriptions used by real-world applications. Finally, we provide a synthetic discussion of the existing approaches, and conclude with a detailed presentation of open research directions.
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Cited by in corpus (5)
- Neural Networks for Entity Matching: A Survey
- Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases
- Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search
- ErGAN: Generative Adversarial Networks for Entity Resolution
- Social Responsibility of Algorithms