Associative Reasoning for Commonsense Knowledge

Associative reasoning refers to the human ability to focus on knowledge that is relevant to a particular problem. In this process, the meaning of symbol names plays an important role: when humans focus on relevant knowledge about the symbol ice, similar symbols like snow also come into focus. In this paper, we model this associative reasoning by introducing a selection strategy that extracts relevant parts from large commonsense knowledge sources. This selection strategy is based on word similarities from word embeddings and is therefore able to take the meaning of symbol names into account. We demonstrate the usefulness of this selection strategy with a case study from creativity testing.