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

Tracking #: 2084-3297

Simon Gottschalk
Elena Demidova

Responsible editor: 
Guest Editors Knowledge Graphs 2018

Submission type: 
Full Paper
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.
Full PDF Version: 


Solicited Reviews:
Click to Expand/Collapse
Review #1
Anonymous submitted on 20/Jan/2019
Review Comment:

This paper describes the details of constructing an event-centric temporal knowledge graph, and shows how the EventKG can be applied for generating biographical timelines. The description of most of the details is clear. It also shows the evaluation of each single step/component and demonstrates the quality of the EventKG.

One suggestion: the title could be further improved by adding temporal knowledge graph.

Review #2
By Enrico Daga submitted on 28/Jan/2019
Review Comment:

I appreciate the modifications made to the article and the accompanying letter that answer all my quesitons and concerns. In my opinion, this version of the article is ready for acceptance.

Review #3
By Francesco Osborne submitted on 10/Feb/2019
Review Comment:

The paper presents a definition of temporal knowledge graph and introduces EventKG, a knowledge base which includes 690K events and over 2.3 million temporal relations. It applies EventKG to the task of generating biographical timelines, showing that it performs better than an alternative approach.

The generation of knowledge graphs of events is a significant topic and it is well addressed in this paper. The research contributions are clear and significant. In particular, EventKG appears to be a useful and potentially influential knowledge base. The approach used for the extraction of EventKG does not appear particularly innovative, but it seems to yield good results.

The authors did a very good job of addressing my comments and those of the other reviewers. This second version is more robust and clear and I recommend to accept it.

Review #4
By Pedro Szekely submitted on 17/Feb/2019
Review Comment:

The authors addressed the suggestions in my original review. The technical details in section 4 are clear now, and the related work updated to cite other efforts to create event knowledge bases.