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TSA-Net: a temporal knowledge graph completion method with temporal-structural adaptation
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AI
DOI：10.1007/s10489-024-05734-1.png)
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Abstract
En 中文
Temporal Knowledge Graph Completion (TKGC) aims to infer missing facts in Temporal Knowledge Graphs (TKGs), where facts are stored along with significant temporal information. However, existing TKGC methods only consider message passing on pairwise relations and fail to capture the complex temporal structural dependencies at the levels of time, predicate and entity in TKGs. To fill this gap, we collect high-frequency patterns in TKGs using mathematical statistics and propose a Temporal-Structural Adaptation Network that is equipped with three specific components, time-component, pred-component, and ent-component, as well as one general component, res-component. Concretely, specific components utilize the time consistency pattern to capture facts with significant regularity in time, and complex structural dependencies in TKGs are handled through predicate concurrency and entity collaboration. Moreover, considering low-frequency and nonoccurrence facts, an additional general component is introduced to make predictions on all entities. The outputs of different components are adaptively fused to vote for the final result. Extensive experiments on six benchmarks demonstrate that our method outperforms state-of-the-art baselines.
Keywords:
Temporal knowledge graphs
Temporal knowledge graph completion
Temporal knowledge graph embedding
Representation learning
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3.5
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APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
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