Exploiting Entity Information for Robust Prediction Over Event Knowledge Graphs

Script event prediction is the task of predicting the subsequent event given a sequence of events that already took place. It benefits task planning and process scheduling for event-centric systems including enterprise systems, IoT systems, etc. Sequence-based and graph-based learning models have been applied to this task. However, when learning data is limited, especially in a multiple-participant-involved enterprise environment, the performance of such models falls short of expectations as they heavily rely on large-scale training data. To take full advantage of given data, in this article we propose a new type of knowledge graph (KG) that models not just events but also entities participating in the events, and we design a collaborative event prediction model exploiting such KGs. Our model identifies semantically similar vertices as collaborators to resolve unknown events, applies gated graph neural networks to extract event-wise sequential features, and exploits a heterogeneous attention network to cope with entity-wise influence in event sequences. To verify the effectiveness of our approach, we designed multiple-choice narrative cloze tasks with inadequate knowledge. Our experimental evaluation with three datasets generated from well-known corpora shows our method can successfully defend against such incompleteness of data and outperforms the state-of-the-art approaches for event prediction.