Research map / Graph learning / Knowledge and temporal graphs
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Know-Evolve’s temporally evolving entity representations and relation-modulated point process instead of static relational models | Entity prediction on GDELT and ICEWS · GDELT and ICEWS sliding-window test sets | MAR | Helped |
| Use Know-Evolve instead of other point-process baselines | Time prediction · GDELT and ICEWS | MAE | Helped |
| Retrain baselines with a sliding training window and incorporate test data | Relational-learning baselines · GDELT and ICEWS sliding-window evaluation | link prediction rank | Helped |
| Use Know-Evolve instead of NTN | Prediction of recurrent and new facts · GDELT-500 | link prediction performance | Helped |
Closest papers by meaning
- DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach (NeurIPS 2024 · similarity 0.90)
- Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning (NeurIPS 2024 · similarity 0.88)
- TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs (NeurIPS 2024 · similarity 0.87)
- TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning (AAAI 2024 · similarity 0.86)
- HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces (AAAI 2024 · similarity 0.85)
- Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence (AAAI 2024 · similarity 0.85)
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