Complex Event Schema Induction with Knowledge-Enriched Diffusion Model (2023)

Abstract: The concept of a complex event schema pertains to the graph structure that represents real-world knowledge of events and their multi-dimensional relationships. However, previous studies on event schema induction have been hindered by challenges such as error propagation and data quality issues. To tackle these challenges, we propose a knowledge-enriched discrete diffusion model. Specifically, we distill the abundant event scenario knowledge of Large Language Models (LLMs) through an object-oriented Python style prompt. We incorporate this knowledge into the training data, enhancing its quality. Subsequently, we employ a discrete diffusion process to generate all nodes and links simultaneously in a non-auto-regressive manner to tackle the problem of error propagation. Additionally, we devise an entity relationship prediction module to complete entity relationships between event arguments. Experimental results demonstrate that our approach achieves outstanding performance across a range of evaluation metrics.

## introduction event schema induction aims to summarize common patterns and structures from historical events. current studies mainly induce the atomic schema for each independent event type and their arguments separately (e.g. "attack" event with the arguments: "attacker", "target", "instrument" and "place"), without considering the correlation between events @xcite @xcite . however, some real-world events are usually very complex, consisting of multiple events and their relations. for example in figure 1 , bombing is a complex event, which involves some fine-grained events, such as assemble, detonate and injure. therefore, some instance graphs buy assemble transport detonate damage broadcast injure die evacuation investigate attack arrest intervention transport victim victim target place place instrument assembler vehicle control event schema per loc per per target veh ie system raw text two police officers arrived at the area. they and three other responding officers subsequently evacuated homes in the area and called in reinforcements, while a sixth officer stayed on the street to redirect pedestrians. the vehicle exploded at 6:30am. eight people were treated at hospitals for injuries and later discharged. at least three vehicles burned after the bombing, at least 41 businesses were damaged, and one building located across the street, away from the site of the bombing, collapsed. researchers attempt to study the complex event schema induction task, which abstracts typical structures for complex events from event data. figure 1 illustrates an example of the complex event schema induction process for the scenario of bombing. initially, an information extraction (ie) tool @xcite is utilized to extract instance graphs from raw texts. subsequently, we induce the event schema based on these extracted instance graphs. the resulting event schema is represented as a graph, where events are interconnected through temporal links (e.g., damage occurs after detonate) and their argument relations (e.g., the target of the detonate event assumes the victim role in the subsequent injure event). however, inducing complex event schema is nontrivial. as shown in figure 1 , it necessitates the model's ability to summarize the events within instance graphs and possess a profound understanding of the multi-dimensional relationships between these events. recently, graph-based methods are proposed for this task by utilizing graph generation techniques @xcite . for example, @xcite proposes an auto-regressive generation method that generates the schema following event temporal order. similarly, @xcite leverages an auto-encoder to encode the global skeleton information and decode the schema graph event by event. despite successful efforts, these methods still face two critical challenges: knowledge coverage of instance graphs: the event schema induction task summarizes the instance graphs to obtain the event schema. thus, the quality of the instance graphs is crucial for the event schema induction. however, the instance graphs are extracted via information extraction (ie) tools @xcite , whose knowledge coverage is very limited. for example, as the representative ie tool, resin @xcite is trained on fixed datasets and can only extract predefined types of entities and events. besides, the extraction performance of resin is also unsatisfactory, which only achieves approximately 64% of f1-score for event detection on the ace dataset. it indicates that the ie tool is difficult to extract complete instance information, even for predefined event types. therefore, how to improve the knowledge coverage of instance graphs is an important problem. ## our approach to solve the complex event schema induction task, we propose a knowledge-enriched discrete diffusion model, as shown in figure 3 . our method mainly consists of three modules: (1) instance graph expansion, which expands the instance graphs using the complex event knowledge obtained from llms atomically. (2) event skeleton induction, which summarizes the event evolution skeleton using a discrete diffusion model. (3) entity relation prediction, which decorates the arguments to the event skeleton and then use a simple graph transformer to predict the entity relations. we will illustrate each component in detail. ## conclusion in this work, we identify the limitations of previous works and proposed a knowledge-enriched discrete diffusion model. to enhance the quality and coherence of the generated schemas, we harness the potential and rich knowledge present in llms by utilizing them for instance graph expansion. our model leverages a discrete diffusion process to learn and generate event skeletons, while incorporating an entity relationship predictor to predict the relationships between event arguments. additionally, we propose a conditional diffusion model with the purpose of generating schemas for mul-tiple diverse topics. we achieved the best results among multiple different evaluation indicators.