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

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bsantraigi/TA_TransE

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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

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

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