文件列表:

5_gpu_version_of_model_for_large_datasets (0, 2022-08-04)
5_gpu_version_of_model_for_large_datasets\model_with5_gpu.py (64603, 2022-08-04)
5_gpu_version_of_model_for_large_datasets\run_with5_gpu.py (22434, 2022-08-04)
__init__.py (0, 2022-08-04)
data (0, 2022-08-04)
data\FB15K237 (0, 2022-08-04)
data\FB15K237\entities.dict (218551, 2022-08-04)
data\FB15K237\entity2id.txt (218559, 2022-08-04)
data\FB15K237\relation2id.txt (15528, 2022-08-04)
data\FB15K237\relations.dict (15522, 2022-08-04)
data\FB15K237\test.txt (1519499, 2022-08-04)
data\FB15K237\train.txt (21277292, 2022-08-04)
data\FB15K237\valid.txt (1303101, 2022-08-04)
data\WN18RR (0, 2022-08-04)
data\WN18RR\entities.dict (603035, 2022-08-04)
data\WN18RR\entity2id.txt (603043, 2022-08-04)
data\WN18RR\relation2id.txt (219, 2022-08-04)
data\WN18RR\relations.dict (214, 2022-08-04)
data\WN18RR\test.txt (113180, 2022-08-04)
data\WN18RR\train.txt (3135377, 2022-08-04)
data\WN18RR\valid.txt (110415, 2022-08-04)
data\WN18RR_v3_ind (0, 2022-08-04)
data\WN18RR_v3_ind\entities.dict (70066, 2022-08-04)
data\WN18RR_v3_ind\entity2id.txt (70073, 2022-08-04)
data\WN18RR_v3_ind\relation2id.txt (219, 2022-08-04)
data\WN18RR_v3_ind\relations.dict (214, 2022-08-04)
data\WN18RR_v3_ind\test.txt (22225, 2022-08-04)
data\WN18RR_v3_ind\train.txt (228490, 2022-08-04)
data\WN18RR_v3_ind\valid.txt (19638, 2022-08-04)
data\WN18RR_v4_ind (0, 2022-08-04)
data\WN18RR_v4_ind\entities.dict (98066, 2022-08-04)
data\WN18RR_v4_ind\entity2id.txt (98073, 2022-08-04)
data\WN18RR_v4_ind\relation2id.txt (164, 2022-08-04)
data\WN18RR_v4_ind\relations.dict (160, 2022-08-04)
data\WN18RR_v4_ind\test.txt (58676, 2022-08-04)
data\WN18RR_v4_ind\train.txt (499274, 2022-08-04)
data\WN18RR_v4_ind\valid.txt (57918, 2022-08-04)
... ...

# GFA-NN ## Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities ### Setup ##### 1. Make sure the Datasets are existent in "./data" ##### 2. Before executing the training, generate pre-preocessed files by running: ./extract_graph_features/process.sh ##### 3. With the current limitation on GPU memories, we made a multi core version (5 gpu cores) to allow running the model on the larger dataset (for example biokg). This version of the model is in the folder: ./5_gpu_version_of_model_for_large_datasets ##### 4. Please check the paper Appendix for the best hyper-parameters. ### Example run: ## Incremental training: WN18RR_INC run for the first dataset: ``` python run_incremental.py --do_train --do_test -save ./experiments/kge_baselines_wn18rr_inc --data_path ./data/WN18RR_inc --data_path_train data/WN18RR_inc/train1.txt -data_path_entities data/WN18RR_inc/entity2id.txt -data_path_rels data/WN18RR_inc/relation2id.txt --model MDE -n 500 -b 1000 -d 200 -g 4.0 -a 2.5 -adv -lr .0005 --max_steps 10000 --test_batch_size 2 --valid_steps 10000 --log_steps 10000 --do_valid -node_feat_path ./data/WN18RR_inc/train_node_features --cuda -psi 14.0 ``` run for the next incoming datasets: for examplle train2.txt and new parameter: -adding_data ``` python run_incremental.py --init_checkpoint -adding_data --do_train --do_test -save ./experiments/kge_baselines_wn18rr_inc2 --data_path ./data/WN18RR_inc --data_path_train data/WN18RR_inc/train2.txt --model MDE -n 500 -b 1000 -d 200 -g 4.0 -a 2.5 -adv -lr .0005 --max_steps 10000 --test_batch_size 2 --valid_steps 10000 --log_steps 10000 --do_valid -node_feat_path ./data/WN18RR_inc/train_node_features --cuda -psi 14.0 ``` ## regular training: WN18RR ``` python run.py --do_train --do_test -save ./experiments/kge_baselines_wn18rr_inc --data_path ./data/WN18RR_inc --model MDE -n 500 -b 1000 -d 200 -g 4.0 -a 2.5 -adv -lr .0005 --max_steps 300000 --test_batch_size 2 --valid_steps 10000 --log_steps 10000 --do_valid -node_feat_path ./data/WN18RR_inc/train_node_features --cuda -psi 14.0 ``` FB15k237: ``` python run.py --do_train --do_test -save ../experiments/kge_baselines_fb237 --data_path ../data/FB15K237 --model MDE -n 1000 -b 1000 -d 200 -g 4.0 -a 2.5 -adv -lr .0005 --max_steps 300000 --test_batch_size 2 --valid_steps 10000 --log_steps 10000 --do_valid -node_feat_path ../data/FB15K237/train_node_features --cuda -psi 15.0 ``` biokg: ``` python run_with5_gpu.py --init_checkpoint ../experiments/kge_baselines_biokg_400_600_850_2 --do_train --do_test -save ../experiments/kge_baselines_biokg_400_600_850 --data_path ../data/biokg --model MDE -n 850 -b 600 -d 400 -g 2.5 -a 2.5 -adv -lr .0005 --max_steps 700000 --test_batch_size 2 --valid_steps 10000 --log_steps 10000 --do_valid -node_feat_path ../data/biokg/train_node_features --cuda -psi 14.0 ``` ## Setup for incremental training: #assume that data cleaning and dubplicates of entities and relations are done. # all the new triples have at least a head or a tail in the known trained entities. #for the first run: #just train on the first coming set. #save the trained model #to make it you can just run for 5 epochs. #incremental training iteration: # load the saved model with its data put it in m1 # new data in new data folder arrives-> generate graph features and dictionary files entities.dic relations.dic #the names of coming files will be like train1.txt train2.txt etc # read the new data: # load them seperatedyly or putting them in one bigger train.txt file? # in this step entity matching must be done, if they are same dedblicate and label the new ones with old ones. #then make a larger dataset, still, only train on newly come entities? or their beghbours too? or all the network? #research question: # to what level of neighbours entites must be train? # can these subgraphs be trained seperatedly? # make a new model name it m2 with size of m1 plus new entities in m2 # copy m1 into m2 # train on new triple t2 + triples of old dataset that have a common entity or relation with t2 # save new model and aggregated triples. ### Dataset Generation for experimetns: for experiments: run create_inc_dataset.py that randomly select triples from train.txt and generates several incoming train files as train1.txt ,train2.txt , ... to run: python create_inc_dataset.py -data_path data/WN18RR_inc -divisions 5 # GFA-NN model training: 1. step extract features: ./extract_graph_features/process.sh 2. step run the embedding: python run_incremental.py --do_train --do_test -save ./experiments/kge_baselines_wn18rr_inc --data_path ./data/WN18RR_inc --train_file train1.txt --model MDE -n 500 -b 1000 -d 200 -g 4.0 -a 2.5 -adv -lr .0005 --max_steps 3000 --test_batch_size 2 --valid_steps 3000 --log_steps 3000 --do_valid -node_feat_path ./data/WN18RR_inc/train_node_features --cuda -psi 14.0 then second run 1. external step 1: data integration using entity matching and deduplicates: there 4 types of triples must be annoated by a 4th column: 1.new: both head tail are new 2.old 3.neghbour 1 : one of the head and tails are new 4.neghbour hop 2 : one of entites are connected to an entity that is old but neighbour to a new entity 2. step extract features: ./extract_graph_features/process.sh 3. with --init_checkpoint to load the saved model and load new train_file: python run_incremental.py --init_checkpoint --do_train --do_test -save ./experiments/kge_baselines_wn18rr_inc --data_path ./data/WN18RR_inc --train_file train2.txt --model MDE -n 500 -b 1000 -d 200 -g 4.0 -a 2.5 -adv -lr .0005 --max_steps 3000 --test_batch_size 2 --valid_steps 3000 --log_steps 3000 --do_valid -node_feat_path ./data/WN18RR_inc/train_node_features --cuda -psi 14.0 Link to the paper on the [ECML conference website is here](https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_1096.pdf). ### FAQ Q: How we reproduce the results of the model for the large dataset? A: Large datasets similar to biokg require a large number of iterations. Since the learning rate reduces during the training we do not suggest setting max_steps to a larger number, instead, we suggest storing the trained model using -save and rerunning the training iteration several times. In our evaluation it executed the training 3 times for biokg. Q: Is the model open for learning furthur features? A: Yes, simply by adding another score and a set of embedding weights to it. Please do not forget to normalize the graph features before learning them.

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