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

## TL;DR

EventKG is a comprehensive, multilingual temporal knowledge graph focusing on events and temporal relations, enabling applications like biographical timeline generation and addressing gaps in existing entity-centric knowledge bases.

## Contribution

The paper introduces EventKG, a large-scale, event-centric temporal knowledge graph, and demonstrates its application in generating biographical timelines using a novel distant supervision approach.

## Key findings

- EventKG contains over 690,000 events and 2.3 million temporal relations.
- The biographical timeline generation method effectively identifies relevant temporal relations.
- EventKG enhances coverage of events and temporal data beyond traditional entity-focused knowledge graphs.

## Abstract

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.