Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving accessibility of the web with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Computer
Community-based game design: experiments on social games for commonsense data collection
Proceedings of the ACM SIGKDD Workshop on Human Computation
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
OntoGame: weaving the semantic web by online games
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Ranking the linked data: the case of DBpedia
ICWE'10 Proceedings of the 10th international conference on Web engineering
Towards exploratory video search using linked data
Multimedia Tools and Applications
Climate quiz: a web application for eliciting and validating knowledge from social networks
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Learning from quizzes using intelligent learning companions
Proceedings of the 22nd international conference on World Wide Web companion
Games with a Purpose or Mechanised Labour?: A Comparative Study
Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies
Crowdsourced Knowledge Acquisition: Towards Hybrid-Genre Workflows
International Journal on Semantic Web & Information Systems
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In 2011 the IBM Computer Watson was beating its human opponents in the American TV quiz show Jeopardy!. However, the questions for the quiz have been developed by a team of human authors. Authoring questions is a difficult task, because in a Jeopardy! game the questions should be neither too easy nor too hard and should fit the general scope of knowledge of the audience and players. Linked Open Data (LOD) provides huge amounts of information that is growing daily. Yet, there is no ranking that determines the importance of LOD facts, as e. g. by querying LOD for movies starring a distinct actor provides numerous answers, whereas it cannot be answered, which of the movies was the most important for this actor. To rank search results for semantic search various heuristics have been developed to cope with the problem of missing rank in the semantic web. This paper proposes a Jeopardy! like quiz game with questions automatically generated from LOD facts to gather ranking information for persons to provide a basis for the evaluation of semantic ranking heuristics.