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        <title>Natural Evolution-based Dual-Level Aggregation for Temporal Knowledge Graph Reasoning</title>
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    <abstract>Temporal knowledge graph (TKG) reasoning aims to predict missing facts based on a given history. Most of the existing methods unifiedly model the evolution process of different events and ignore their inherent asynchronous characteristics, resulting in suboptimal performance. To tackle this challenge, we propose a Natural Evolution-based Dual-level Aggregation framework (NEDA) for TKG reasoning. Specifically, we design a natural division strategy to group TKGs into different patches according to the occurrence of a given target entity. Then, we present a dual-level aggregation scheme to extract local representations from information within patches and then aggregate these representations with adaptive weights as the final entity representations. By assigning varying weights to different patches, this aggregation scheme can incorporate the asynchronous characteristics of event evolution for representation computation, thus enhancing prediction performance. Extensive experiments demonstrate the significant improvement of our proposed model.</abstract>
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%0 Conference Proceedings
%T Natural Evolution-based Dual-Level Aggregation for Temporal Knowledge Graph Reasoning
%A Chen, Bin
%A Xiao, Chunjing
%A Zhou, Fan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chen-etal-2024-natural
%X Temporal knowledge graph (TKG) reasoning aims to predict missing facts based on a given history. Most of the existing methods unifiedly model the evolution process of different events and ignore their inherent asynchronous characteristics, resulting in suboptimal performance. To tackle this challenge, we propose a Natural Evolution-based Dual-level Aggregation framework (NEDA) for TKG reasoning. Specifically, we design a natural division strategy to group TKGs into different patches according to the occurrence of a given target entity. Then, we present a dual-level aggregation scheme to extract local representations from information within patches and then aggregate these representations with adaptive weights as the final entity representations. By assigning varying weights to different patches, this aggregation scheme can incorporate the asynchronous characteristics of event evolution for representation computation, thus enhancing prediction performance. Extensive experiments demonstrate the significant improvement of our proposed model.
%U https://aclanthology.org/2024.findings-emnlp.543
%P 9274-9284

Markdown (Informal)

[Natural Evolution-based Dual-Level Aggregation for Temporal Knowledge Graph Reasoning](https://aclanthology.org/2024.findings-emnlp.543) (Chen et al., Findings 2024)

• Natural Evolution-based Dual-Level Aggregation for Temporal Knowledge Graph Reasoning (Chen et al., Findings 2024)

ACL

• Bin Chen, Chunjing Xiao, and Fan Zhou. 2024. Natural Evolution-based Dual-Level Aggregation for Temporal Knowledge Graph Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9274–9284, Miami, Florida, USA. Association for Computational Linguistics.