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.

Usage

Prerequisites

• Python 3.6
• Tensorflow 1.4.0

For NaLP, publised in WWW'19

Prepare data

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

Training

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

Evaluation

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.

For the extended methods, published in TKDE'21

NaLP is extended to tNaLP, NaLP⁺, and then tNaLP⁺, respectively.

tNaLP

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.

NaLP⁺

train_only.py and see_eval.py with --batching_postfix _plus, i.e., model.py and batching_plus.py, are used.

tNaLP⁺

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.

Citation

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}
}

Related work

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network