# OntoED: Low-resource Event Detection with Ontology Embedding arXiv: https://arxiv.org/abs/2105.10922 ยท HTML: https://mlsanity.com/paper/2105.10922/ ## Key facts - Cited by 23 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 40 references that are in the map. - First posted on arXiv 23 May 2021. ## Builds on (most cited first, 16 of 16) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017) - [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020) - [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020) - [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018) - [Zero-Shot Transfer Learning for Event Extraction](https://mlsanity.com/paper/1707.01066/) (2017) - [Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning](https://mlsanity.com/paper/1903.08948/) (2019) - [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019) - [Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection](https://mlsanity.com/paper/1910.11621/) (2019) - [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020) - [Temporal Common Sense Acquisition with Minimal Supervision](https://mlsanity.com/paper/2005.04304/) (2020) - [Extensively Matching for Few-shot Learning Event Detection](https://mlsanity.com/paper/2006.10093/) (2020) - [Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction](https://mlsanity.com/paper/2009.07373/) (2020) - [MLBiNet: A Cross-Sentence Collective Event Detection Network](https://mlsanity.com/paper/2105.09458/) (2021) - [Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection](https://mlsanity.com/paper/2105.09509/) (2021) - [A logic-based relational learning approach to relation extraction: The OntoILPER system](https://mlsanity.com/paper/2001.04192/) (2020) ## Built on by (most cited first, 23 of 23) - [OntoProtein: Protein Pretraining With Gene Ontology Embedding](https://mlsanity.com/paper/2201.11147/) (2022) - [AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba](https://mlsanity.com/paper/2106.01686/) (2021) - [TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction](https://mlsanity.com/paper/2311.09562/) (2023) - [Ontology-enhanced Prompt-tuning for Few-shot Learning](https://mlsanity.com/paper/2201.11332/) (2022) - [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021) - [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023) - [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://mlsanity.com/paper/2201.03335/) (2022) - [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024) - [Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering](https://mlsanity.com/paper/2410.04752/) (2024) - [Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering](https://mlsanity.com/paper/2203.07633/) (2022) - [Information Extraction in Low-Resource Scenarios: Survey and Perspective](https://mlsanity.com/paper/2202.08063/) (2022) - [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023) - [GLEAN: Active Generalized Category Discovery with Diverse LLM Feedback](https://mlsanity.com/paper/2502.18414/) (2025) - [LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training](https://mlsanity.com/paper/2112.01404/) (2021) - [Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study](https://mlsanity.com/paper/2210.10678/) (2022) - [Few-shot Event Detection: An Empirical Study and a Unified View](https://mlsanity.com/paper/2305.01901/) (2023) - [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025) - [Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection](https://mlsanity.com/paper/2305.14163/) (2023) - [Improve Event Extraction via Self-Training with Gradient Guidance](https://mlsanity.com/paper/2205.12490/) (2022) - [EDM3: Event Detection as Multi-task Text Generation](https://mlsanity.com/paper/2305.16357/) (2023) - [SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres](https://mlsanity.com/paper/2305.13617/) (2023) - [Recouple Event Field via Probabilistic Bias for Event Extraction](https://mlsanity.com/paper/2305.11498/) (2023) - [PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning](https://mlsanity.com/paper/2404.17877/) (2024) ## Abstract (arXiv) Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.