  The evolution and development of events have their own basic principles,
which make events happen sequentially. Therefore, the discovery of such
evolutionary patterns among events are of great value for event prediction,
decision-making and scenario design of dialog systems. However, conventional
knowledge graph mainly focuses on the entities and their relations, which
neglects the real world events. In this paper, we present a novel type of
knowledge base - Event Logic Graph (ELG), which can reveal evolutionary
patterns and development logics of real world events. Specifically, ELG is a
directed cyclic graph, whose nodes are events, and edges stand for the
sequential, causal, conditional or hypernym-hyponym (is-a) relations between
events. We constructed two domain ELG: financial domain ELG, which consists of
more than 1.5 million of event nodes and more than 1.8 million of directed
edges, and travel domain ELG, which consists of about 30 thousand of event
nodes and more than 234 thousand of directed edges. Experimental results show
that ELG is effective for the task of script event prediction.
