Cyc: toward programs with common sense
Communications of the ACM
Probabilistic reasoning in decision support systems: from computation to common sense
Probabilistic reasoning in decision support systems: from computation to common sense
Open Mind Common Sense: Knowledge Acquisition from the General Public
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Extracting and evaluating general world knowledge from the Brown corpus
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
Verbosity: a game for collecting common-sense facts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An analysis of knowledge collected from volunteer contributors
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Are your participants gaming the system?: screening mechanical turk workers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A computer-in-the-loop approach for detecting bullies in the classroom
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Adaptive game for reducing aggressive behavior
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
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Collecting commonsense knowledge from freely available text can reduce the cost and effort of creating large knowledge bases. For the acquired knowledge to be useful, we must ensure that it is correct, and that it carries information about its relevance and about the context in which it can be considered commonsense. In this paper, we design, and evaluate an online game that classifies, using the input from players, text extracted from the web as either commonsense knowledge, domain-specific knowledge, or nonsense. A continuous scale is defined to classify the knowledge as nonsense or commonsense and it is later used during the evaluation of the data to identify which knowledge is reliable and which one needs further qualification. When comparing our results to other similar knowledge acquisition systems, our game performs better with respect to coverage, redundancy, and reliability of the commonsense acquired.