EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation
This addresses the problem of incomplete event knowledge for semantic analytics in web, news, and social media contexts, though it is incremental as it builds on existing knowledge graphs.
The authors tackled the lack of comprehensive event and temporal relation coverage in existing knowledge graphs by creating EventKG, a multilingual event-centric temporal knowledge graph with over 690,000 events and 2.3 million temporal relations, and applied it to biographical timeline generation with a distant supervision method, showing effectiveness in evaluation.
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.