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

• Main Authors: Calegari, Gloria Re, Fiano, Andrea, Celino, Irene
• Format: Journal Article
• Language: English
• Published: 07-11-2018
• Subjects: Computer Science - Human-Computer Interaction
• DOI: 10.48550/arxiv.1811.02848

With the rise of linked data and knowledge graphs, the need becomes
compelling to find suitable solutions to increase the coverage and correctness
of 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 partial
gold standard, i.e. some "ground truth" to train automatic models. Gold
standards are manually constructed, either by involving domain experts or by
adopting crowdsourcing and human computation solutions.
In this paper, we present an open source software framework to build Games
with a Purpose for linked data refinement, i.e. web applications to crowdsource
partial ground truth, by motivating user participation through fun incentive.
We detail the impact of this new resource by explaining the specific data
linking "purposes" supported by the framework (creation, ranking and validation
of links) and by defining the respective crowdsourcing tasks to achieve those
goals.
To show this resource's versatility, we describe a set of diverse
applications that we built on top of it; to demonstrate its reusability and
extensibility potential, we provide references to detailed documentation,
including an entire tutorial which in a few hours guides new adopters to
customize and adapt the framework to a new use case.