ELG: An Event Logic Graph
This addresses the need for event-based knowledge representation in domains like finance and travel, offering a tool for event prediction and dialog systems, though it appears incremental as it builds on knowledge graph concepts.
The paper tackles the problem of modeling event evolution for prediction and decision-making by introducing the Event Logic Graph (ELG), a novel knowledge base that captures sequential, causal, conditional, and is-a relations between events, with experiments showing its effectiveness for script event prediction.
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.