  Humans use countless basic, shared facts about the world to efficiently
navigate in their environment. This commonsense knowledge is rarely
communicated explicitly, however, understanding how commonsense knowledge is
represented in different paradigms is important for both deeper understanding
of human cognition and for augmenting automatic reasoning systems. This paper
presents an in-depth comparison of two large-scale resources of general
knowledge: ConcpetNet, an engineered relational database, and SWOW a knowledge
graph derived from crowd-sourced word associations. We examine the structure,
overlap and differences between the two graphs, as well as the extent to which
they encode situational commonsense knowledge. We finally show empirically that
both resources improve downstream task performance on commonsense reasoning
benchmarks over text-only baselines, suggesting that large-scale word
association data, which have been obtained for several languages through
crowd-sourcing, can be a valuable complement to curated knowledge graphs

Computation and Language