VLP

To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion

Requirements

• pytorch == 1.9.0
• dgl-cu111
• graph_tool

Data

• entities.dict: a dictionary map entities to unique ids
• relations.dict: a dictionary map relations to unique ids
• train.txt: the KGE model is trained to fit this data set
• valid.txt: create a blank file if no validation data is available
• test.txt: the KGE model is evaluated on this data set

Models

• RotatE-VLP
• ComplEx-VLP
• DistMult-VLP

Usage

All training commands are listed in best_config.sh. For example, you can run the following commands to train RotatE-VLP on WN18RR and FB15k-237 datasets.

# WN18RR
bash run.sh train RotatE wn18rr 0 0 512 1024 500 4.0 0.5 0.00005 80000 8 1 0.5 5.0 8 0.5 1.1 10 30000 -de

# FB15k-237
bash run.sh train RotatE FB15k-237 0 0 1024 256 1000 11.0 1.0 0.0005 100000 16 -1 0.5 0.5 5 3.0 1.7 13 40000 -de

Acknowledgement

We refer to the code of RotatE. Thanks for their contributions.