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    <abstract>Temporal Knowledge Graph Reasoning (TKGR) aims to predict future facts based on historical data. Current mainstream models primarily use embedding techniques, which predict missing facts by representing entities and relations as low-dimensional vectors. However, these models often consider only the structural information of individual entities and relations, overlooking the broader structure of the entire TKG. To address these limitations, we propose a novel model called Relation Logical Reasoning and Relation-aware Entity Encoding (RLEE), drawing inspiration from attention mechanisms and logical rule-based techniques. RLEE introduces a two-layer representation of the TKG: an entity layer and a relation layer. At the relation layer, we extract relation paths to mine potential logical correlations between different relations, learning relation embeddings through a process of relation logical reasoning. At the entity layer, we use the relation-aware attention mechanism to learn the entity embeddings specific to the predicted query relations. These learned relation and entity embeddings are then used to predict facts at future timestamps. When evaluated on five commonly used public datasets, RLEE consistently outperforms state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T Relation Logical Reasoning and Relation-aware Entity Encoding for Temporal Knowledge Graph Reasoning
%A Liu, Longzhou
%A Xiao, Chenglong
%A Wang, Shanshan
%A Liu, Tingwen
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F liu-etal-2025-relation
%X Temporal Knowledge Graph Reasoning (TKGR) aims to predict future facts based on historical data. Current mainstream models primarily use embedding techniques, which predict missing facts by representing entities and relations as low-dimensional vectors. However, these models often consider only the structural information of individual entities and relations, overlooking the broader structure of the entire TKG. To address these limitations, we propose a novel model called Relation Logical Reasoning and Relation-aware Entity Encoding (RLEE), drawing inspiration from attention mechanisms and logical rule-based techniques. RLEE introduces a two-layer representation of the TKG: an entity layer and a relation layer. At the relation layer, we extract relation paths to mine potential logical correlations between different relations, learning relation embeddings through a process of relation logical reasoning. At the entity layer, we use the relation-aware attention mechanism to learn the entity embeddings specific to the predicted query relations. These learned relation and entity embeddings are then used to predict facts at future timestamps. When evaluated on five commonly used public datasets, RLEE consistently outperforms state-of-the-art baselines.
%U https://aclanthology.org/2025.coling-main.88/
%P 1323-1332

Markdown (Informal)

[Relation Logical Reasoning and Relation-aware Entity Encoding for Temporal Knowledge Graph Reasoning](https://aclanthology.org/2025.coling-main.88/) (Liu et al., COLING 2025)

• Relation Logical Reasoning and Relation-aware Entity Encoding for Temporal Knowledge Graph Reasoning (Liu et al., COLING 2025)

ACL

• Longzhou Liu, Chenglong Xiao, Shanshan Wang, and Tingwen Liu. 2025. Relation Logical Reasoning and Relation-aware Entity Encoding for Temporal Knowledge Graph Reasoning. In Proceedings of the 31st International Conference on Computational Linguistics, pages 1323–1332, Abu Dhabi, UAE. Association for Computational Linguistics.