Before using QM-mod you need to train a TKG reasoning model. You can use any knowledge graph embedding framework for training. We used the PyTorch version of OpenKE.
To use a previously trained TKG reasoning model with QM-mod you need to export the entity, relation and timestamps matrices as numpy arrays using pickle:
- For TTransE and TA-TransE/TA-DistMult the matrices are called 'entities.p', 'relations.p' and 'timestamp.p'
Furthermore you need to provide one files: 'Now-2022', which contain a mention text mapping of quadruple corresponding index (id) in the embedding files.
The trained 200D ComplEx embeddings can be obtained from(https://github.com/haseebs/haseebs.github.io/raw/master/assets/embeddings.zip).
Follow the instructions to install OpenKE. Under openke_scripts you can find exemplary training scripts (make sure you update the paths to the dataset in OpenKE format). As the API of OpenKE frequently changes, scripts work with the following version of OpenKE.
Word embeddings are required for training the extension. You may obtain the wikipedia2vec word embeddings from here. An example config file to reproduce the results is provided as config.ini in the root folder.
For training: python owe/run_QM_mod.py -t -c <PATH_TO_DATASET> -d <PATH_TO_CONFIG_AND_OUTPUT_DIR> --<KGC_MODEL_NAME> <PATH_TO_KGC_EMBEDDINGS>