MA-TPath: Multi-Hop Temporal knowledge Graph Reasoning with Mulit-Agent Reinforcement Learning This repository contains the implementation of the MA-TPath architectures described in the paper.
- Install Tensorflow (>= 1.1.0)
- Python 3.x (tested on Python 3.6)
- Numpy
- Pandas
- Scikit-learn
- tqdm
After installing the requirements, run the following command to reproduce results for MA-TPath:
$ python trainer.py --base_output_dir output/{dataset-name} --path_length {path-length>1} --hidden_size {hidden_size} --embedding_size {embedding_size} --batch_size {batch_size} --beta 0.05 --Lambda 0.05 --use_entity_embeddings 1 --train_entity_embeddings 1 --train_relation_embeddings 1 --train_tim_embeddings 1 --data_input_dir {datasets-dir} --vocab_dir {datasets-dir-vocab} --model_load_dir null --load_model 0 --total_iterations {total_iterations} --nell_evaluation 0
To run MA-TPath on a custom graph based dataset, you would need the graph and the queries as 4-triple in the form of (e1,r, e2,tim). Where e1, and e2 are nodes connected by the edge r. The vocab can of the dataset can be created using the create_vocab.py file found in data/data preprocessing scripts. The vocab needs to be stores in the json format {'entity/relation/tim': ID}.
| MINERVA| link|
| Baselines | Code |
|---|---|
| TransE / TransH | link |
| DistMult | link |
| MINERVA | link |
| TTransE | link |
| HyTE | link |
| TA-TransE / TA-DistMult | link |
| DE-TransE / DE-DistMult / DE-SimplE | link |
| RE-NET | link |
| TPmod | link |