Using Hierarchical Skills for Optimized Task Assignment in Knowledge-Intensive Crowdsourcing

Panagiotis Mavridis (1, 2, 3) , David Gross-Amblard (1, 2, 3) , Zoltán Miklós (3, 1, 2)

1 UR - Université de Rennes
2 DRUID - Declarative & Reliable management of Uncertain, user-generated Interlinked Data
3 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires

Besides the simple human intelligence tasks such as image labeling, crowdsourcing platforms propose more and more tasks that require very specific skills, especially in participative science projects. In this context, there is a need to reason about the required skills for a task and the set of available skills in the crowd, in order to increase the resulting quality. Most of the existing solutions rely on unstructured tags to model skills (vector of skills). In this paper we propose to finely model tasks and participants using a skill tree, that is a taxonomy of skills equipped with a similarity distance within skills. This model of skills enables to map participants to tasks in a way that exploits the natural hierarchy among the skills. We illustrate the effectiveness of our model and algorithms through extensive experimentation with synthetic and real data sets.

Mots clés: skill modeling; crowdsourcing; task mapping

Domaines: Informatique [cs]

Panagiotis Mavridis, David Gross-Amblard, Zoltán Miklós. Using Hierarchical Skills for Optimized Task Assignment in Knowledge-Intensive Crowdsourcing. WWW2016, ACM, IW3C, Apr 2016, Montreal, Canada. ⟨10.1145/2872427.2883070⟩. ⟨hal-01306481⟩