End-to-End Entity Resolution for Big Data: A Survey [article]

Vassilis Christophides, Vasilis Efthymiou, Themis Palpanas, George Papadakis, Kostas Stefanidis
2020 arXiv   pre-print
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
more » ... 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.
arXiv:1905.06397v3 fatcat:rs2qoolz2jcppklriew5pjfefq