Archiv der Kategorie: crowdsourcing

Crowdsourcing Landscape

A Semantic Web Vocabulary for Describing Enterprise Crowdsourcing Activities

Currently, I am working towards a semantic web vocabulary for capturing, storing, and sharing enterprise crowdsourcing data. The main purpose of the vocabulary is to offer a mechanism to embed structured crowdsourcing data into a webpage along with other existing displaying information. Crowdsourcing intermediaries and other business applications can then extract, combine, and filter the machine-readable crowdsourcing data and use it for further processing. Adding semantics about a crowdsourcing activity to a webpage and not relying on displaying information only can increase the visibility of crowdsourcing tasks for potential contributors both inside and outside the company.

What are the main objectives that guide the development of this vocabulary?

  1. to provide a controlled vocabulary of concepts and relationships that crowdsourcing users, software developers and architects as well as decision makers commonly understand
  2. to enable semantic and intelligent search, navigation as well as browsing support for crowdsourcing tasks
  3. to facilitate interoperability that is not only guaranteed by the controlled vocabulary itself but also by specifying the equivalence to concepts and properties of other semantic web standards, such as FOAF, GoodRelations, or Dublin Core
  4. to check consistency and to support the verification and validation of crowdsourcing data (e.g., to check data types and value ranges of data properties)
  5. to provide a foundation and a support for the configuration of crowdsourcing tasks (e.g., requesters can share and reuse well established task specifications of a particular type of crowdsourcing activity)

To reach the goal, I want to set some general conditions that seems to be reasonable for developing a practically useful semantic web vocabulary:

  1. The vocabulary should be compatible with existing W3C standards and recommendations, such as RDF and OWL.
  2. The vocabulary should be independent from different syntax, e.g., Microdata, RDFa, Turtle.
  3. The vocabulary should include concepts and relations that can not only be processed by machines but are also human-readable, lightweight and simple to use.
  4. The vocabulary should be made public available and royalty-free to increase the dissemination.