Humans interact with each other using their collection of commonsense knowledge about everyday concepts and their relationships. To establish a similar natural form of interaction with computers, they should be given the same collection of knowledge. Various research works have focused on building large-scale commonsense knowledge that computers can use. But capturing and representing commonsense knowledge into a machine-usable repository, whether manual or automated, are still far from completion. This research explores an approach to acquiring commonsense knowledge through the use of children's stories. Relation extraction templates are also utilized to store the learned knowledge into an ontology, which can then be used by automatic story generators and other applications with children as the target users.
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