Microtask crowdsourcing has shown to be a successful method for engaging humans to accomplish tasks that, even simple and repetitive, cannot so effectively be carried out by fully automatic techniques. With an increasing micro-labor supply and a larger available workforce, new microtask platforms have emerged providing an extensive list of marketplaces where microtasks are offered by requesters and completed by crowd workers.
One of the aspects of micro work that remains under discussion is the process of quality assurance. Besides methods to identify inappropriate actions (on both sides of crowd work), finding a suitable match between available microtasks and crowd workers has been acknowledged as a promising way to improve the quality of crowd work. The current microtask crowdsourcing infrastructure does not offer the possibility to be recognized for already accomplished and offered work in different microtask platforms. This lack of information leads to uninformed decisions in selection processes.
To overcome this limitation we are developing Crowd Work CV, an approach to bring Curriculum Vitae into microtask crowdsourcing.
The Crowd Work CV data model is based on RDF. It represents crowdsourcing agents' identities and promote their work experience across the different microtask marketplaces.
Crowd Work CV contains information about interests, qualifications and work history.
You will find the Crowd Work CV ontology in the [ontology] (https://github.com/criscod/CrowdWorkCV/tree/master/ontology) folder. It has been specified in OWL and its latest version is v1.0.
If you would like to read further documentation about the approach, you will find scientific papers and other documents in the [publications] (https://github.com/criscod/CrowdWorkCV/tree/master/publications) folder.
This work is under a CC BY-SA (Creative Commons Attribution ShareAlike) license. See also: explanation See also: legal code
If you have questions or comments, please write an email to Cristina Sarasua ([email protected]).
The research leading to these results has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 611242 Sense4Us