A Turing Game for Commonsense Knowledge Extraction By

Commonsense is of primary interest to AI research since the inception of the field. Traditionally, commonsense knowledge is gathered by using humans to create and insert it in knowledge bases. Automating the collection of commonsense from text that is freely available can reduce the cost and effort of creating large knowledge bases and can enable systems that dynamically adapt to current relevant commonsense. In this thesis, we design, implement and evaluate an online game that classifies, with players’ input, 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 released on the Web and on Facebook. It is open for play 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.