  The ability to understand and generate similes is an imperative step to
realize human-level AI. However, there is still a considerable gap between
machine intelligence and human cognition in similes, since deep models based on
statistical distribution tend to favour high-frequency similes. Hence, a
large-scale symbolic knowledge base of similes is required, as it contributes
to the modeling of diverse yet unpopular similes while facilitating additional
evaluation and reasoning. To bridge the gap, we propose a novel framework for
large-scale simile knowledge base construction, as well as two probabilistic
metrics which enable an improved understanding of simile phenomena in natural
language. Overall, we construct MAPS-KB, a million-scale probabilistic simile
knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB
corpora. We conduct sufficient experiments to justify the effectiveness and
necessity of the methods of our framework. We also apply MAPS-KB on three
downstream tasks to achieve state-of-the-art performance, further demonstrating
the value of MAPS-KB.
