The Concept Game: Better Commonsense Knowledge Extraction by Combining Text Mining and a Game with a Purpose

Common sense collection has long been an important subfield of AI. This paper introduces a combined architecture for commonsense harvesting by text mining and a game with a purpose. The text miner module uses a seed set of known facts (sampled from ConceptNet) as training data and produces candidate commonsense facts mined from corpora. The game module taps humans' knowledge about the world by letting them play a simple slot-machine-like game. The proposed system allows us to collect significantly better commonsense facts than the state-of-the-art text miner alone, as shown experimentally for 5 rather different types of commonsense relations.