A Turing Game for Commonsense Knowledge Extraction

Collecting commonsense from text with the aid of a game can reduce the cost and effort of creating large knowledge bases. In this paper, we design, implement, and evaluate an online game that classifies, with input from players, text extracted from the Web as commonsense knowledge, domain-specific knowledge or nonsense. We also create a knowledge base that includes commonsense facts in natural language and information on how common a given fact is. The game is currently available for play on the Web and on Facebook, and under constant improvement. The creation of a continuous scale to classify commonsense helped during evaluation of the data by clearly identifying which knowledge is reliable and which needs further qualification. When comparing our results to other similar knowledge acquisition systems, our Turing Game performs better with respect to coverage,redundancy, and reliability of the commonsense acquired.