  Computational and cognitive studies of event understanding suggest that
identifying, comprehending, and predicting events depend on having structured
representations of a sequence of events and on conceptualizing (abstracting)
its components into (soft) event categories. Thus, knowledge about a known
process such as "buying a car" can be used in the context of a new but
analogous process such as "buying a house". Nevertheless, most event
understanding work in NLP is still at the ground level and does not consider
abstraction. In this paper, we propose an Analogous Process Structure Induction
APSI framework, which leverages analogies among processes and conceptualization
of sub-event instances to predict the whole sub-event sequence of previously
unseen open-domain processes. As our experiments and analysis indicate, APSI
supports the generation of meaningful sub-event sequences for unseen processes
and can help predict missing events.
