The GWAP Enabler is an open source software framework that allows to design and develop GWAP web applications to solve linked data refinement issues.
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.
The GWAP Enabler is 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. The GWAP Enabler addresses specific data linking "purposes" (creation, ranking and validation of links) by embedding the respective crowdsourcing tasks to achieve those goals within the gameplay.
The GWAP Enabler has been adopted to implement a set of diverse applications (e.g. Indomilando, Land Cover Validation Game, Night Knights) that demonstrate its reusability and extensibility potential; detailed documentation is available, including an entire tutorial which in a few hours guides new adopters to customize and adapt the framework to a new use case.
To cite the GWAP Enabler, please use the following reference:
Gloria Re Calegari, Andrea Fiano, Irene Celino: A Framework to build Games with a Purpose for Linked Data Refinement, in proceedings of the International Semantic Web Conference 2018, Resources Track, Monterey, California, 2018.
The design and development of the GWAP Enabler has been partially supported by the EU H2020 project STARS4ALL.