NaLP and its Extended Methods: Link Prediction on N-ary Relational Data Based on Relatedness Evaluation
This project provides the tensorflow implementation of the link prediction method NaLP on n-ary relational data based on relatedness evaluation, and its extended methods, published in WWW'19 and TKDE'21, respectively.
- Python 3.6
- Tensorflow 1.4.0
Transform the representation form of facts for JF17K. Transform each value sequence in JF17K to a set of role-value pairs:
python JF17K2rv_json.py
Build data before training and test for JF17K and WikiPeople:
python builddata.py --sub_dir JF17K_version1 --dataset_name JF17K_version1
python builddata.py --sub_dir WikiPeople --dataset_name WikiPeople
Build data for filtering the right facts in negative sampling or computing the filtered metrics when evaluation:
python builddata.py --sub_dir JF17K_version1 --dataset_name JF17K_version1 --if_permutate True --bin_postfix _permutate
python builddata.py --sub_dir WikiPeople --dataset_name WikiPeople --if_permutate True --bin_postfix _permutate
To train NaLP:
python train_only.py --sub_dir JF17K_version1 --dataset_name JF17K_version1 --wholeset_name JF17K_version1_permutate --model_name JF17K_version1_opt --embedding_dim 100 --n_filters 200 --n_gFCN 1000 --batch_size 128 --learning_rate 0.00005 --n_epochs 5000 --saveStep 100
python train_only.py --sub_dir WikiPeople --dataset_name WikiPeople --wholeset_name WikiPeople_permutate --model_name WikiPeople_opt --embedding_dim 100 --n_filters 200 --n_gFCN 1200 --batch_size 128 --learning_rate 0.00005 --n_epochs 5000 --saveStep 100
Files see_eval.py and see_eval_bi-n.py provide four evaluation metrics, including the Mean Reciprocal Rank (MRR), Hits@1, Hits@3 and Hits@10 in filtered setting. In these two files, parameter --valid_or_test indicates whether to evaluate NaLP in the validation set (set to 1) or test set (set to 2).
To evaluate NaLP in the validation set (JF17K lacks a validation set):
python see_eval.py --sub_dir WikiPeople --dataset_name WikiPeople --wholeset_name WikiPeople_permutate --model_name WikiPeople_opt --embedding_dim 100 --n_filters 200 --n_gFCN 1200 --batch_size 128 --n_epochs 5000 --start_epoch 100 --evalStep 100 --valid_or_test 1 --gpu_ids 0,1,2,3
To evaluate NaLP in the test set:
python see_eval.py --sub_dir JF17K_version1 --dataset_name JF17K_version1 --wholeset_name JF17K_version1_permutate --model_name JF17K_version1_opt --embedding_dim 100 --n_filters 200 --n_gFCN 1000 --batch_size 128 --n_epochs 5000 --start_epoch 100 --evalStep 100 --valid_or_test 2 --gpu_ids 0,1,2,3
python see_eval.py --sub_dir WikiPeople --dataset_name WikiPeople --wholeset_name WikiPeople_permutate --model_name WikiPeople_opt --embedding_dim 100 --n_filters 200 --n_gFCN 1200 --batch_size 128 --n_epochs 5000 --start_epoch 100 --evalStep 100 --valid_or_test 2 --gpu_ids 0,1,2,3
File see_eval_bi-n.py provides more detailed results on binary and n-ary relational facts. It is used in the same way as see_eval.py.
Note that, it takes a lot of time to evaluate NaLP, since we need to compute a score via NaLP for each candidate (each value/role in the value/role set). To speed up the evaluation process, see_eval.py and see_eval_bi-n.py are implemented in a multi-process manner.
NaLP is extended to tNaLP, NaLP+, and then tNaLP+, respectively.
train_only.py and see_eval.py with --model_postfix _type and --batching_postfix _bce, i.e., model_type.py and batching_bce.py, are used.
train_only.py and see_eval.py with --batching_postfix _plus, i.e., model.py and batching_plus.py, are used.
train_only.py and see_eval.py with --model_postfix _type and --batching_postfix _plus_bce, i.e., model_type.py and batching_plus_bce.py, are used.
If you found this codebase or our works useful, please cite:
@inproceedings{NaLP,
title={Link prediction on n-ary relational data},
author={Guan, Saiping and Jin, Xiaolong and Wang, Yuanzhuo and Cheng, Xueqi},
booktitle={Proceedings of the 28th International Conference on World Wide Web (WWW'19)},
year={2019},
pages={583--593}
}
@article{NaLP_extend,
title={Link prediction on n-ary relational data based on relatedness evaluation},
author={Guan, Saiping and Jin, Xiaolong and Guo, Jiafeng and Wang, Yuanzhuo and Cheng, Xueqi},
booktitle={IEEE Transactions on Knowledge and Data Engineering (TKDE)},
year={2021}
}
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