Refining Linked Data with Games with a Purpose

Abstract

With the rise of linked data and knowledge graphs, the need becomes compelling to find suitable solutions to increase the coverage and correctness of data sets, to add missing knowledge and to identify and remove errors. Several approaches -mostly relying on machine learning and natural language processing 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. We also introduce our approach for incremental truth inference over the contributions provided by players of Games with a Purpose (also abbreviated as GWAP): we motivate the need for such a method with the specificity of GWAP vs. traditional crowdsourcing; we explain and formalize the proposed process, explain its positive consequences and illustrate the results of an experimental comparison with stateof-the-art approaches. 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. C o r r e c t e d P r o o f Data Intelligence Refining Linked Data with Games with a Purpose

Human Computation vs. Machine Learning: an Experimental Comparison for Image Classification

“…also (Celino et al, 2012)). By design, a minimum of four agreeing users are required to reach the classification threshold; more details on the design and implementation of Night Knights are provided in (Celino et al, 2016).…”