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With the rise of linked data and knowledge graphs, the need becomescompelling to find suitable solutions to increase the coverage and correctnessof datasets, to add missing knowledge and to identify and remove errors.Several approaches - mostly relying on machine learning and NLP techniques -have been proposed to address this refinement goal; they usually need a partialgold standard, i.e. some "ground truth" to train automatic models. Goldstandards are manually constructed, either by involving domain experts or byadopting crowdsourcing and human computation solutions.  In this paper, we present an open source software framework to build Gameswith a Purpose for linked data refinement, i.e. web applications to crowdsourcepartial ground truth, by motivating user participation through fun incentive.We detail the impact of this new resource by explaining the specific datalinking "purposes" supported by the framework (creation, ranking and validationof links) and by defining the respective crowdsourcing tasks to achieve thosegoals.  To show this resource's versatility, we describe a set of diverseapplications that we built on top of it; to demonstrate its reusability andextensibility potential, we provide references to detailed documentation,including an entire tutorial which in a few hours guides new adopters tocustomize and adapt the framework to a new use case.

A Framework to build Games with a Purpose for Linked Data Refinement