Details
| Original language | English |
|---|---|
| Pages (from-to) | 1039-1070 |
| Number of pages | 32 |
| Journal | Semantic web |
| Volume | 10 |
| Issue number | 6 |
| Publication status | Published - 28 Oct 2019 |
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.
Keywords
- biographical timelines, Events, knowledge graph
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Networks and Communications
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In: Semantic web, Vol. 10, No. 6, 28.10.2019, p. 1039-1070.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - EventKG
T2 - the hub of event knowledge on the web – and biographical timeline generation
AU - Gottschalk, Simon
AU - Demidova, Elena
N1 - Funding Information: This work was partially funded by the EU Horizon 2020 under ERC grant “ALEXANDRIA” (339233) and MSCA-ITN-2018 “Cleopatra” (812997), the Federal Ministry of Education and Research (BMBF) under “Data4UrbanMobility” (02K15A040) and “Simple-ML” (01IS18054).
PY - 2019/10/28
Y1 - 2019/10/28
N2 - 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.
AB - 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.
KW - biographical timelines
KW - Events
KW - knowledge graph
UR - http://www.scopus.com/inward/record.url?scp=85066886867&partnerID=8YFLogxK
U2 - 10.3233/SW-190355
DO - 10.3233/SW-190355
M3 - Article
AN - SCOPUS:85066886867
VL - 10
SP - 1039
EP - 1070
JO - Semantic web
JF - Semantic web
SN - 1570-0844
IS - 6
ER -