A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution
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Abstract (arXiv)
Completion through the embedding representation of the knowledge graph (KGE) has been a research hotspot in recent years. Realistic knowledge graphs are mostly related to time, while most of the existing KGE algorithms ignore the time information. A few existing methods directly or indirectly encode the time information, ignoring the balance of timestamp distribution, which greatly limits the performance of temporal knowledge graph completion (KGC). In this paper, a temporal KGC method is proposed based on the direct encoding time information framework, and a given time slice is treated as the finest granularity for balanced timestamp distribution. A large number of experiments on temporal knowledge graph datasets extracted from the real world demonstrate the effectiveness of our method.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 30 Aug 2021.
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11 of 26 references.
References · 11
- 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
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- 2016Complex Embeddings for Simple Link Predictioncited by 902
- 2017Convolutional 2D Knowledge Graph Embeddingscited by 730
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- 2018A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalizationcited by 30
- 2014Open Question Answering with Weakly Supervised Embedding Modelscited by 26
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.