Event Ontology Completion with Hierarchical Structure Evolution Networks (2023)

Abstract: Traditional event detection methods require predefined event schemas. However, manually defining event schemas is expensive and the coverage of schemas is limited. To this end, some works study the event type induction (ETI) task, which discovers new event types via clustering. However, the setting of ETI suffers from two limitations: event types are not linked into the existing hierarchy and have no semantic names. In this paper, we propose a new research task named Event Ontology Completion (EOC), which aims to simultaneously achieve event clustering, hierarchy expansion and type naming. Furthermore, we develop a Hierarchical Structure Evolution Network (HalTon) for this new task. Specifically, we first devise a Neighborhood Contrastive Clustering module to cluster unlabeled event instances. Then, we propose a Hierarchy-Aware Linking module to incorporate the hierarchical information for event expansion. Finally, we generate meaningful names for new types via an In-Context Learning-based Naming module. Extensive experiments indicate that our method achieves the best performance, outperforming the baselines by 8.23%, 8.79% and 8.10% of ARI score on three datasets.

## introduction automated real-world event detection is a crucial task towards mining fast-evolving event knowledge. existing methods @xcite @xcite typically require a pre-defined event schema along with massive human-labeled data for model learning. despite the tremendous success, manually defining an event schema is especially expensive and labor-intensive, which requires experts to examine amounts of raw data in advance to specify potential event types. besides, as new events are happening every day @xcite @xcite , it is neither realistic nor scalable to define all event schemas in advance. to get rid of the above problems, some researchers study the task of event type induction (eti), which aims to discover new event types from an input corpus @xcite @xcite . the task is generally formulated as a clustering problem, where each cluster represents an event type (cf. figure 1(a) ). existing methods typically utilize probabilistic generative models @xcite , adhoc clustering algorithms @xcite or neural networks @xcite @xcite to induce event clusters. despite these successful efforts for clustering, the eti setting inevitably suffers from two limitations in real applications: event types are not linked into the existing hierarchy: these methods only divide unlabeled event instances into several isolated clusters, without linking newly discovered types to an existing event ontology (i.e., an event hierarchy) 2 . some studies about human cognition find that people tend to organize real-world events in a hierarchical way @xcite , ranging from coarse-grained (i.e., top-level) events to finegrained (i.e., bottom-level) events. moreover, the ontologies of most knowledge bases also adopt hierarchical organization forms of event types @xcite . the hierarchical forms represent events at different granularity and abstraction levels, which helps people quickly understand related scenarios. for example, according to the event hierarchy in figure 1 (b), we can easily gain the overall picture of the justice scenario, which may involve multiple events, such as sue, arrest and sentence. therefore, it is very necessary to establish and maintain the event hierarchy. however, since new events emerge rapidly and incessantly, it is impractical to manually add newly discovered types into the event ontology. therefore, how to automatically expand the existing event hierarchy with new event types is an important problem. event types have no semantic names: most eti methods only assign numbers (i.e., type number) to the new event types, and lack the ability to generate human-readable type names. to enable new event types to be used in downstream tasks, it is inevitable to assign meaningful names for them in advance. for example, the event type name is required for training event extraction models @xcite and constructing event knowledge graphs @xcite . although the event type name is important, previous studies only focus on event clustering and ignore the type naming @xcite @xcite . as a result, the discovered event types cannot be directly applied to downstream applications, and extra human efforts are needed to conduct secondary labeling for the new types. thus, how to automatically generate meaningful names for new event types is also a problem worth exploring. in the light of the above restrictions, we propose a new task named event ontology completion (eoc). given a set of unlabeled event instances, 2 event ontology denotes the hierarchical organization structure of known event types, which is usually incomplete. the task requires that the model simultaneously achieves the following goals: (1) event clustering: dividing the unlabeled instances into several clusters; (2) hierarchy expansion: linking new event types (i.e., predicted clusters) into an existing event hierarchy; and (3) type naming: generating semantically meaningful names for new event types. as shown in figure 1(b ), the eoc model aims to divide the unlabeled instances into three clusters, and link the clusters to the root and justice node of the event hierarchy. meanwhile, the three new event types are named life, sue and arrest, respectively. compared to eti, eoc requires models to complete the event ontology, instead of only event clustering. therefore, the proposed task is more useful and practical, but it is also more challenging. to this end, we propose a novel method named hierarchical structure evolution network (halton) for this new task. concretely, we first devise a neighborhood contrastive clustering module for event clustering. the module utilizes a neighborhood contrastive loss to boost clustering for both supervised and unsupervised data. intuitively, in a semantic feature space, neighboring instances should have a similar type, and pulling them together makes clusters more compact. then, we propose a hierarchy-aware linking module for hierarchy expansion. the module uses a dynamic path-based margin loss to integrate the hierarchical information into event representations. compared with the static margin, the dynamic margin can capture the semantic similarities of event types in the hierarchy, which is conducive to hierarchy expansion. finally, we design an in-context learningbased naming module for type naming. the module elicits the abstraction ability of large language models (llms) via in-context learning to generate human-readable names for discovered event types. extensive experiments on three datasets show that our proposed method brings significant improvements over baselines. to summarize, our contributions are: (1) as a seminal study, we propose a new research task named event ontology completion, and introduce baselines and evaluation metrics for three task settings, including event clustering, hierarchy expansion and type naming. (2) we devise a novel method named hierarchical structure evolution network (halton), which achieves task goals via the collaboration of three components, namely neighborhood contrastive clustering, hierarchy-aware linking and in-context learning-based naming. it can serve as a strong baseline for the research on the task. (3) experimental results indicate that our method substantially outperforms baselines, achieving 8.23%, 8.79% and 8.10% improvements of ari score on three datasets. ## task formulation the eoc task assumes that there is an incomplete event ontology t , which is constructed by experts in advance. the ontology is a tree-like structure, where leaf nodes denote known event types. given an unlabeled dataset d @xmath0, the goals of eoc include: (1) event clustering, dividing the unlabeled instances into m u groups; (2) hierarchy expansion, linking each cluster c to the corresponding position of the hierarchy t ; and (3) type naming, generating a human-readable name for each cluster c. following @xcite , we use golden triggers for event clustering foot_0 . to enable the model to achieve the above goals, we leverage a labeled dataset d @xmath1, y l i )} n @xmath2. the types set of the labeled dataset is denoted as y l . the event types in y l belong to known types, which correspond to the leaf nodes of the event ontology t . ## methodology figure 2 shows the overall architecture of hal-ton, which consists of three major components: (1) neighborhood contrastive clustering ( §3.1), which learns discriminative representations for event clustering; (2) hierarchy-aware linking ( §3.2), which attaches newly discovered event types to the existing event hierarchy; and (3) in-context learning-based naming ( §3.3), which generates event type names via in-context learning. we will illustrate each component in detail. ## related work although event extraction has met with remarkable success @xcite @xcite @xcite , it usually requires that hand-crafted event schemas and annotations are given in advance. since manually defining event schemas is labor-intensive and fails to generalize to new scenarios, some researchers have attempted to explore the eti task @xcite @xcite @xcite @xcite . typical approaches utilize probabilistic generative models @xcite , adhoc clustering techniques @xcite and neural networks @xcite to induce event clusters. @xcite study the event profiling task and utilizes a bayesian generative model to obtain clusters. @xcite design an unsupervised method to generate salient event types by clus-tering predicate-object pairs. recently, @xcite propose a co-training framework to combine abstraction-based and token-based representations for the task. despite these successful efforts, existing methods cannot link new event types to the existing ontology, and lack the ability to generate meaningful names for new event types. ## conclusion in this paper, we define a new event ontology completion task, aiming at simultaneously achieving event clustering, hierarchy expansion and type naming. furthermore, we propose a hierarchical structure evolution network (halton), which achieves the goals via collaboration between neighborhood contrastive clustering, hierarchy-aware linking and in-context learning-based naming. experimental results on three datasets show that our method brings significant improvements over baselines.