# Event Linking: Grounding Event Mentions to Wikipedia

arXiv: https://arxiv.org/abs/2112.07888 · HTML: https://mlsanity.com/paper/2112.07888/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [EventKG: A Multilingual Event-Centric Temporal Knowledge Graph](https://mlsanity.com/paper/1804.04526/) (2018)
- [WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia](https://mlsanity.com/paper/2104.05022/) (2021)
- [Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas](https://mlsanity.com/paper/2010.11333/) (2020)

## Built on by (most cited first, 11 of 11)

- [EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs](https://mlsanity.com/paper/2404.00209/) (2024)
- [NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing](https://mlsanity.com/paper/2305.10709/) (2023)
- [Multilingual Event Linking to Wikidata](https://mlsanity.com/paper/2204.06535/) (2022)
- [FAMuS: Frames Across Multiple Sources](https://mlsanity.com/paper/2311.05601/) (2023)
- [Building Interpretable and Reliable Open Information Retriever for New Domains Overnight](https://mlsanity.com/paper/2308.04756/) (2023)
- [An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph](https://mlsanity.com/paper/2312.02334/) (2023)
- [Argument-Aware Approach To Event Linking](https://mlsanity.com/paper/2403.15097/) (2024)
- [Lost in Recursion: Mining Rich Event Semantics in Knowledge Graphs](https://mlsanity.com/paper/2404.16405/) (2024)
- [Hierarchical Event Grounding](https://mlsanity.com/paper/2302.04197/) (2023)
- [WikiCausal: Corpus and Evaluation Framework for Causal Knowledge Graph Construction](https://mlsanity.com/paper/2409.00331/) (2024)
- [Enhancing Event Candidate Acquisition for Event Linking](https://mlsanity.com/paper/2609.13670/) (2026)

## Abstract (arXiv)

Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: how can the reader obtain more knowledge about this particular event in addition to what is provided by the local context in the article? This work defines Event Linking, a new natural language understanding task at the event level. Event linking tries to link an event mention appearing in an article to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event mention refers to. To standardize the research in this new direction, we contribute in four-fold. First, this is the first work in the community that formally defines Event Linking task. Second, we collect a dataset for this new task. Specifically, we automatically gather training set from Wikipedia, and then create two evaluation sets: one from the Wikipedia domain, reporting the in-domain performance, and a second from the real-world news domain, to evaluate out-of-domain performance. Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system EVELINK to set a competitive result for the new task. Fourth, we conduct a detailed and insightful analysis to help understand the task and the limitation of the current model. Overall, as our analysis shows, Event Linking is a considerably challenging and essential task requiring more effort from the community. Data and code are available here: https://github.com/CogComp/event-linking.