Recent advances in web technologies allow people to help solve
complex problems by performing online tasks in return for money, learning, or
fun. At present, human contribution is limited to the tasks defined on individual
crowdsourcing platforms. Furthermore, there is a lack of tools and technologies
that support matching of tasks with appropriate users, across multiple systems.
A more explicit capture of the semantics of crowdsourcing tasks could enable
the design and development of matchmaking services between users and tasks.
The paper presents the SLUA ontology that aims to model users and tasks in
crowdsourcing systems in terms of the relevant actions, capabilities, and rewards. This model describes different types of human tasks that help in solving
complex problems using crowds. The paper provides examples of describing
users and tasks in some real world systems, with SLUA ontology