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STKGR-PR

Requirements

  • python3 (tested on 3.6.6)
  • pytorch (tested on 1.5.0)

Datasets

there are six datasets under folder data.

# dataset ICEWS14-2%
data/ICEWS14-2

# dataset ICEWS14-3%
data/ICEWS14-3

# dataset ICEWS14-5%
data/ICEWS14-5

# dataset ICEWS05-15-0.3%
data/ICEWS515-03

# dataset ICEWS05-15-0.4%
data/ICEWS515-04

# dataset ICEWS05-15-0.5%
data/ICEWS515-05

Data Processing

./experiment.sh configs/<dataset>.sh --process_data <gpu-ID>

dataset is the name of datasets. In our experiments, dataset could be ICEWS14-2, ICEWS14-3, ICEWS14-5, ICEWS515-03, ICEWS515-04 and ICEWS515-05. <gpu-ID> denotes a non-negative integer that serves as the index for identifying a specific GPU.

Pretrain Temporal Knowledge Graph Embedding

./experiment-emb.sh configs/<dataset>-<model>.sh --train <gpu-ID>

dataset is the name of datasets and model is the name of temporal knowledge graph embedding model. In our experiments, dataset could be ICEWS14-2, ICEWS14-3, ICEWS14-5, ICEWS515-03, ICEWS515-04 and ICEWS515-05, model could be conve. <gpu-ID> denotes a non-negative integer that serves as the index for identifying a specific GPU.

Train

# take ICEWS05-15-0.3% for example
./experiment-rs.sh configs/icews515-03-rs.sh --train <gpu-ID>

Test

# take ICEWS05-15-0.3% for example
./experiment-rs.sh configs/icews515-03-rs.sh --inference <gpu-ID>

Acknowledgement

We refer to the code of [DacKGR](Xin Lv, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Wei Zhang, Yichi Zhang, Hao Kong, Suhui Wu. Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph. The Conference on Empirical Methods in Natural Language Processing (EMNLP 2020).). Thanks for their contributions.