Modeling Historical Relevant and Local Frequency Context for Representation-Based Temporal Knowledge Graph Forecasting

Shengzhe Zhang, Wei Wei, Rikui Huang, Wenfeng Xie, Dangyang Chen


Anthology ID:
2024.findings-emnlp.451
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7675–7686
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.451
DOI:
Bibkey:
Cite (ACL):
Shengzhe Zhang, Wei Wei, Rikui Huang, Wenfeng Xie, and Dangyang Chen. 2024. Modeling Historical Relevant and Local Frequency Context for Representation-Based Temporal Knowledge Graph Forecasting. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7675–7686, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Modeling Historical Relevant and Local Frequency Context for Representation-Based Temporal Knowledge Graph Forecasting (Zhang et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-emnlp.451.pdf
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