# TimeMachine: Timeline Generation for Knowledge-Base Entities

arXiv: https://arxiv.org/abs/1502.04662 · HTML: https://mlsanity.com/paper/1502.04662/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2015.

## Built on by (most cited first, 3 of 3)

- [COfEE: A Comprehensive Ontology for Event Extraction from text](https://mlsanity.com/paper/2107.10326/) (2021)
- [QuoteKG: A Multilingual Knowledge Graph of Quotes](https://mlsanity.com/paper/2207.09562/) (2022)
- [Blocks Architecture (BloArk): Efficient, Cost-Effective, and Incremental Dataset Architecture for Wikipedia Revision History](https://mlsanity.com/paper/2410.04410/) (2024)

## Abstract (arXiv)

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.