Year: 2017
Type: conference-paper
Abstract: In Entity Resolution, the objective is to find which records of a dataset refer to the same real-world entity. Crowd Entity Resolution uses humans, in addition to machine algorithms, to improve the quality of the outcome. We study a hybrid approach that combines two common interfaces for human tasks in Crowd Entity Resolution, taking into account key observations about the advantages and disadvantages of the two interfaces. We give a formal definition to the problem of human task selection and we derive algorithms with strong optimality guarantees. Our experiments with four real-world datasets show that our hybrid approach gives an improvement of 50% to 300% in the crowd cost to resolve a dataset, compared to using a single interface.
Institutions: Stanford University, University of California San Diego
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
Funder: National Science Foundation
OpenAlex ID: W2612964472