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Related Questions
- What are some common challenges in training RNNs on sequential data with long-term dependencies in knowledge graphs?
- How do RNNs struggle to capture complex temporal relationships in knowledge graphs with multiple entities and events?
- What are some techniques to address the vanishing gradient problem in RNNs when modeling temporal relationships in knowledge graphs?
- Can LSTMs or GRUs improve the performance of RNNs in modeling temporal relationships in knowledge graphs with long-term dependencies?
- What are some strategies to handle out-of-vocabulary (OOV) words or entities in RNNs when modeling temporal relationships in knowledge graphs?
- How can attention mechanisms be applied to RNNs to improve their ability to model complex temporal relationships in knowledge graphs?
- What are some alternatives to RNNs for modeling temporal relationships in knowledge graphs, such as graph neural networks or transformers?
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