  Theory of mind (ToM) refers to humans' ability to understand and infer the
desires, beliefs, and intentions of others. The acquisition of ToM plays a key
role in humans' social cognition and interpersonal relations. Though
indispensable for social intelligence, ToM is still lacking for modern AI and
NLP systems since they cannot access the human mental state and cognitive
process beneath the training corpus. To empower AI systems with the ToM ability
and narrow the gap between them and humans, in this paper, we propose COKE: the
first cognitive knowledge graph for machine theory of mind. Specifically, COKE
formalizes ToM as a collection of 45k+ manually verified cognitive chains that
characterize human mental activities and subsequent behavioral/affective
responses when facing specific social circumstances. In addition, we further
generalize COKE using LLMs and build a powerful generation model COLM tailored
for cognitive reasoning. Experimental results in both automatic and human
evaluation demonstrate the high quality of COKE, the superior ToM ability of
COLM, and its potential to significantly enhance social applications.
