mlsanityData to 7 Oct 2026

OntoED: Low-resource Event Detection with Ontology Embedding

arXiv 2105.10922 ↗First posted 23 May 2021cs.IR cs.AI cs.CLcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 16 of its 40 references that are in the map. First posted on arXiv 23 May 2021.

Builds on

16 of 40 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  3. 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
  4. 2020MAVEN: A Massive General Domain Event Detection Datasetcited by 103
  5. 2018Jointly Multiple Events Extraction via Attention-based Graph Information Aggregationcited by 73
  6. 2017Zero-Shot Transfer Learning for Event Extractioncited by 66
  7. 2019Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoningcited by 63
  8. 2019Joint Reasoning for Temporal and Causal Relationscited by 60
  9. 2019Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detectioncited by 54
  10. 2020Joint Constrained Learning for Event-Event Relation Extractioncited by 53
  11. 2020Temporal Common Sense Acquisition with Minimal Supervisioncited by 38
  12. 2020Extensively Matching for Few-shot Learning Event Detectioncited by 18
4 more
  1. 2020Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extractioncited by 15
  2. 2021MLBiNet: A Cross-Sentence Collective Event Detection Networkcited by 15
  3. 2021Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detectioncited by 13
  4. 2020A logic-based relational learning approach to relation extraction: The OntoILPER systemcited by 1

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Citing papers per year: 2021: 3; 2022: 7; 2023: 8; 2024: 3; 2025: 2.

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  1. 2022OntoProtein: Protein Pretraining With Gene Ontology Embeddingcited by 70
  2. 2021AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibabacited by 33
  3. 2023TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extractioncited by 27
  4. 2022Ontology-enhanced Prompt-tuning for Few-shot Learningcited by 24
  5. 2021What is Event Knowledge Graph: A Surveycited by 19
  6. 2023The Devil is in the Details: On the Pitfalls of Event Extraction Evaluationcited by 18
  7. 2022DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Populationcited by 17
  8. 2024Ontology Embedding: A Survey of Methods, Applications and Resourcescited by 12
  9. 2024Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answeringcited by 12
  10. 2022Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clusteringcited by 11
  11. 2022Information Extraction in Low-Resource Scenarios: Survey and Perspectivecited by 10
  12. 2023MOKA: Moral Knowledge Augmentation for Moral Event Extractioncited by 7
11 more
  1. 2025GLEAN: Active Generalized Category Discovery with Diverse LLM Feedbackcited by 6
  2. 2021LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-trainingcited by 5
  3. 2022Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Studycited by 5
  4. 2023Few-shot Event Detection: An Empirical Study and a Unified Viewcited by 5
  5. 2025Event Extraction in Large Language Modelcited by 5
  6. 2023Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detectioncited by 4
  7. 2022Improve Event Extraction via Self-Training with Gradient Guidancecited by 3
  8. 2023EDM3: Event Detection as Multi-task Text Generationcited by 3
  9. 2023SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspherescited by 2
  10. 2023Recouple Event Field via Probabilistic Bias for Event Extractioncited by 0
  11. 2024PromptCL: Improving Event Representation via Prompt Template and Contrastive Learningcited by 0