COLING 2022: Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference
This is the C++ implementation of our approach EngineKG. We propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules.
You can download all the datasets employed in our experiments from Drive.
In order to reproduce the results of EngineKG model, taking fb15k dataset for an instance, you can kindly run the following commands:
g++ Train_EngineKG.cpp -o Train_EngineKG
./Train_EngineKG -lr 0.0005 -epoch 100 -margin 1.5 -margin_p 1.5 -margin_r 2.5 -res_path dc_loop -data_dir fb15k
In order to evaluate the EngineKG model, you can kindly run the following commands:
g++ Test_EngineKG.cpp -o Test_EngineKG
./Test_EngineKG -res_path dc_loop -data_dir fb15k -hit_n 10
If you use the codes, please cite the following paper:
@inproceedings{niu2022enginekg,
author = {Guanglin Niu and
Bo Li and
Yongfei Zhang and
Shiliang Pu},
title = {Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference},
booktitle = {COLING},
year = {2022}
}