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GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method
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DOI：10.1109/TKDE.2023.3334165.png)
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摘要
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Temporal Knowledge Graph (TKG) representation learning embeds entities and event types into a continuous low-dimensional vector space by integrating the temporal information, which is essential for downstream tasks, e.g., event prediction and question answering. Existing methods stack multiple graph convolution layers to model the influence of distant entities, leading to the over-smoothing problem. To alleviate the problem, recent studies infuse reinforcement learning to obtain paths that contribute to modeling the influence of distant entities. However, due to the limited number of hops, these studies fail to capture the correlation between entities that are far apart and even unreachable. To this end, we propose GTRL, an entity Group-aware Temporal knowledge graph Representation Learning method. GTRL is the first work that incorporates the entity group modeling to capture the correlation between entities by stacking only a finite number of layers. Specifically, the entity group mapper is proposed to generate entity groups from entities in a learning way. Based on entity groups, the implicit correlation encoder is introduced to capture implicit correlations between any pairwise entity groups. In addition, the hierarchical GCNs are exploited to accomplish the message aggregation and representation updating on the entity group graph and the entity graph. Finally, GRUs are employed to capture the temporal dependency in TKGs. Extensive experiments on six real-world datasets demonstrate that GTRL achieves the state-of-the-art performances on the event prediction task, outperforming the best baseline by an average of 7.35%, 6.09%, 8.31%, and 11.21% in MRR, Hits@1, Hits@3, and Hits@10, respectively.
Keyword:
Representation learning
Correlation
Task analysis
Knowledge graphs
Convolution
Stacking
Tail
Temporal knowledge graph
representation learning
entity group modeling
graph convolution network
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10.4
论文数:
6.9K
被引数:
3.2W
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