Code framework for Temporal KG completion.

Implementation of Temporal Knowledge Graph Completion following the work of Duran et al., Learning Sequence Encoders for Temporal Knowledge Graph Completion

• Code:

  • modify based on knowledge_representation_pytorch

  • 1st column in train.txt - subject entity

  • 2nd column - relation

  • 3rd column - object entity

  • 4th column - time

  • 1st figure in stat.txt - number of entities

  • 2nd figure in stat.txt - number of relations

  used preprocess_TA_step1.py and preprocess_TA_step2.py to make data for TA_TransE.

  python preprocess_TA_step1.py ICEWS14
  python preprocess_TA_step2.py ICEWS14
  

  Note : data is already Preprocessed. if you have new data then you can follow the above Process i.e. "python preprocess_TA_step1.py ICEWS14"

• TATransE.py : train code

• You can run the code with

   python TA_TransE.py ICEWS14
   
  

  eg:

   cd ./baselines
   CUDA_VISIBLE_DEVICES=0 python TA_TransE.py -f 1 -d ICEWS14 -L 1 -bs 1024 -n 1000
  
  

Note: when you run the code like python "TA_TransE.py ICEWS14", you need to give dataset in argument with out _TA