Crowdsourcing and the Semantic Web (Dagstuhl Seminar 14282)

Abraham Bernstein, Jan Marco Leimeister, Natasha Noy, Cristina Sarasua, Elena Simperl
2014
Semantic technologies provide flexible and scalable solutions to master and make sense of an increasingly vast and complex data landscape. However, while this potential has been acknowledged for various application scenarios and domains, and a number of success stories exist, it is equally clear that the development and deployment of semantic technologies will always remain reliant of human input and intervention. This is due to the very nature of some of the tasks associated with the semantic
more » ... ata management life cycle, which are famous for their knowledge-intensive and/or context-specific character; examples range from conceptual modeling in almost any flavor, to labeling resources (in different languages), describing their content in terms of ontological terms, or recognizing similar concepts and entities. For this reason, the Semantic Web community has always looked into applying the latest theories, methods and tools from CSCW (Computer Supported Cooperative Work), participatory design, Web 2.0, social computing, and, more recently crowdsourcing to find ways to engage with users and encourage their involvement in the execution of technical tasks. Existing approaches include the usage of wikis as semantic content authoring environments, leveraging folksonomies to create formal ontologies, but also human computation approaches such as games with a purpose or micro-tasks. This document provides a summary of the Dagstuhl Seminar 14282: Crowdsourcing and the Semantic Web, which in July 2014 brought together researchers of the emerging scientific community at the intersection of crowdsourcing and Semantic Web technologies. We collect the position statements written by the participants of seminar, which played a central role in the discussions about the evolution of our research field. Abstract Semantic technologies provide flexible and scalable solutions to master and make sense of an increasingly vast and complex data landscape. However, while this potential has been acknowledged for various application scenarios and domains, and a number of success stories exist, it is equally clear that the development and deployment of semantic technologies will always remain reliant of human input and intervention. This is due to the very nature of some of the tasks associated with the semantic data management life cycle, which are famous for their knowledgeintensive and/or context-specific character; examples range from conceptual modeling in almost any flavor,
doi:10.5167/uzh-100648 fatcat:tgby2rfbyzd3bltjhvkkimsjr4