@inproceedings{zhang-etal-2021-eventke-event,
title = "{E}vent{KE}: Event-Enhanced Knowledge Graph Embedding",
author = "Zhang, Zixuan and
Wang, Hongwei and
Zhao, Han and
Tong, Hanghang and
Ji, Heng",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.120",
doi = "10.18653/v1/2021.findings-emnlp.120",
pages = "1389--1400",
abstract = "Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.",
}
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<abstract>Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.</abstract>
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%0 Conference Proceedings
%T EventKE: Event-Enhanced Knowledge Graph Embedding
%A Zhang, Zixuan
%A Wang, Hongwei
%A Zhao, Han
%A Tong, Hanghang
%A Ji, Heng
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F zhang-etal-2021-eventke-event
%X Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.
%R 10.18653/v1/2021.findings-emnlp.120
%U https://aclanthology.org/2021.findings-emnlp.120
%U https://doi.org/10.18653/v1/2021.findings-emnlp.120
%P 1389-1400
Markdown (Informal)
[EventKE: Event-Enhanced Knowledge Graph Embedding](https://aclanthology.org/2021.findings-emnlp.120) (Zhang et al., Findings 2021)
ACL
- Zixuan Zhang, Hongwei Wang, Han Zhao, Hanghang Tong, and Heng Ji. 2021. EventKE: Event-Enhanced Knowledge Graph Embedding. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1389–1400, Punta Cana, Dominican Republic. Association for Computational Linguistics.