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Modeling Knowledge Graphs with Composite Reasoning

This is the code of paper Modeling Knowledge Graphs with Composite Reasoning.

Wanyun Cui, Linqiu Zhang. AAAI 2024

1. Results

The results of CompilE_D and CompilE_N on WN18RR, FB15k237, UMLS and KINSHIP are as follows.

<p align="center"> <img src="./table3.png" style="width:600px;height:370px;"> </p> <p align="center"> <img src="./table4.png" style="width:700px;height:350px;"> </p>

2. Reproduce the Results

To reproduce the above results, download the pkl files for the four datasets here.

Move those pkl files to the current directory, and run the following commands.

#################################### WN18RR ####################################
# CompilE_N
python learn.py --dataset WN18RR --model ComplEx --rank 2000 \
--optimizer Adagrad --learning_rate 1e-1 --batch_size 100 --use_N3 --use_N3_weight 0.1 \
--save_path trained_models/wn/dura \
--max_epochs 100 --valid 2 --data_path data \
--regularizer composite --mode_list "hrt-t-hrt;hrt-h-hrt" \
--n_pos 10 --w3 0.6 --fully_train

# CompilE_D
python learn.py --dataset WN18RR --model ComplEx --rank 2000 \
--optimizer Adagrad --learning_rate 1e-1 --batch_size 100 --use_DURA_W --use_DURA_W_weight 0.1 \
--save_path trained_models/wn/dura \
--max_epochs 100 --valid 2 --data_path data \
--regularizer composite --mode_list "hrt-t-hrt;hrt-h-hrt" \
--n_pos 10 --w3 0.4 --fully_train --do_ce_weight

#################################### FB237 ####################################
# CompilE_N
python learn.py --dataset FB237 --model ComplEx --rank 2000 \
--optimizer Adagrad --learning_rate 1e-1 --batch_size 100 --use_N3 --use_N3_weight 0.05 \
--save_path trained_models/umls/dura \
--max_epochs 100 --valid 2 --data_path data \
--regularizer composite --mode_list "hrt-hr-rt;hrt-rt-hr" \
--n_pos 10 --w2 0.2 --fully_train


# CompilE_D
python learn.py --dataset FB237 --model ComplEx --rank 2000 \
--optimizer Adagrad --learning_rate 1e-1 --batch_size 200 --use_DURA_W --use_DURA_W_weight 0.05 \
--save_path trained_models/umls/dura \
--max_epochs 100 --valid 2 --data_path data \
--regularizer composite --mode_list "hrt-hr-rt;hrt-rt-hr" \
--n_pos 10 --w2 0.1 --fully_train


#################################### UMLS ####################################
# CompilE_N
python learn.py --dataset umls --model ComplEx --rank 2000 \
--optimizer Adagrad --learning_rate 1e-1 --batch_size 100 --use_N3 --use_N3_weight 0.005 \
--save_path trained_models/umls/N3 \
--max_epochs 100 --valid 2 --data_path data \
--regularizer composite --mode_list "hrt-hr-rt;hrt-rt-hr" \
--n_pos 10 --w2 1.0 --fully_train

# CompilE_D
python learn.py --dataset umls --model ComplEx --rank 2000 \
--optimizer Adagrad --learning_rate 5e-2 --batch_size 100 --use_DURA_W --use_DURA_W_weight 0.001 \
--save_path trained_models/umls/dura \
--max_epochs 100 --valid 2 --data_path data \
--regularizer composite --mode_list "hrt-hr-rt;hrt-rt-hr" \
--n_pos 10 --w2 0.8 --fully_train


#################################### KINSHIP ####################################
# CompilE_N
python learn.py --dataset kinship --model ComplEx --rank 2000 \
--optimizer Adagrad --learning_rate 1e-1 --batch_size 100 --use_N3 --use_N3_weight 0.05 \
--save_path trained_models/kinship/N3 \
--max_epochs 100 --valid 2 --data_path data \
--regularizer composite --mode_list "hrt-rt-hrt;hrt-hr-hrt;hrt-t-hrt;hrt-h-hrt;hrt-hr-t;hrt-rt-h" \
--n_pos 10 --w1 0.4 --w3 0.4 --fully_train

# CompilE_D
python learn.py --dataset kinship --model ComplEx --rank 2000 \
--optimizer Adagrad --learning_rate 5e-2 --batch_size 100 --use_DURA_W --use_DURA_W_weight 0.005 \
--save_path trained_models/kinship/dura \
--max_epochs 100 --valid 2 --data_path data \
--regularizer composite --mode_list "hrt-hr-rt;hrt-rt-hrt" \
--n_pos 10 --w2 1.0 --fully_train

Acknowledgement

We refer to the code of kbc and DURA. Thanks for their contributions.