# EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation

arXiv: https://arxiv.org/abs/1905.08794 · HTML: https://mlsanity.com/paper/1905.08794/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 24 of 24)

- [Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning](https://mlsanity.com/paper/2104.10353/) (2021)
- [Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.00327/) (2021)
- [Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2203.07782/) (2022)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2111.00276/) (2021)
- [GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale](https://mlsanity.com/paper/2108.13092/) (2021)
- [Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs](https://mlsanity.com/paper/2107.13257/) (2021)
- [Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation](https://mlsanity.com/paper/2210.12902/) (2022)
- [Synthesizing Event-centric Knowledge Graphs of Daily Activities Using Virtual Space](https://mlsanity.com/paper/2307.16206/) (2023)
- [Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2303.14898/) (2023)
- [Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2210.04490/) (2022)
- [OEKG: The Open Event Knowledge Graph](https://mlsanity.com/paper/2302.14688/) (2023)
- [SERC: Syntactic and Semantic Sequence based Event Relation Classification](https://mlsanity.com/paper/2111.02265/) (2021)
- [QuoteKG: A Multilingual Knowledge Graph of Quotes](https://mlsanity.com/paper/2207.09562/) (2022)
- [Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering](https://mlsanity.com/paper/2305.04522/) (2023)
- [ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in Biographical Reasoning](https://mlsanity.com/paper/2509.22991/) (2025)
- [Creating Knowledge Graphs for Geographic Data on the Web](https://mlsanity.com/paper/2302.08823/) (2023)
- [LaSER: Language-Specific Event Recommendation](https://mlsanity.com/paper/2303.04712/) (2023)
- [Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing](https://mlsanity.com/paper/2303.04794/) (2023)
- [Rediscovering Ranganathan: A Prismatic View of His Life through the Knowledge Graph Spectrum](https://mlsanity.com/paper/2401.03343/) (2023)
- [Question Answering Over Spatio-Temporal Knowledge Graph](https://mlsanity.com/paper/2402.11542/) (2024)
- [HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities](https://mlsanity.com/paper/2605.09348/) (2026)
- [A Multi-Domain and Multi-Task Generative Framework with Explicit Task and Domain Conditioning for Cross-Domain Event Extraction](https://mlsanity.com/paper/2608.23235/) (2026)
- [AI Historian: Helping historians organize and verify person-centred temporal clues from dispersed historical narratives](https://mlsanity.com/paper/2608.29133/) (2026)

## Abstract (arXiv)

One of the key requirements to facilitate the semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events, entities and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. In this article we address this limitation, formalise the concept of a temporal knowledge graph and present its instantiation - EventKG. EventKG is a multilingual event-centric temporal knowledge graph that incorporates over 690 thousand events and over 2.3 million temporal relations obtained from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical RDF representation. Whereas popular entities often possess hundreds of relations within a temporal knowledge graph such as EventKG, generating a concise overview of the most important temporal relations for a given entity is a challenging task. In this article we demonstrate an application of EventKG to biographical timeline generation, where we adopt a distant supervision method to identify relations most relevant for an entity biography. Our evaluation results provide insights on the characteristics of EventKG and demonstrate the effectiveness of the proposed biographical timeline generation method.