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Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities

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# 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:
```steps
1. 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
2. read the new data:
 load them separately or put them in one bigger train.txt file?
 In this step, entity matching must be done. If they are the same, deduplicate and label the new ones with the old ones.
 Then, make a larger dataset, still only train on newly arrived entities, or their neighbors too, or the entire network?

Research question: 
 to what level of neighbour entites must be trained?
 Can these subgraphs be trained separately?

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 experiments:
for experiments: run create_inc_dataset.py that randomly selects 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 on the 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.

**Citation**
To use the code or the proposed idea of the paper, cite the paper:

```
@InProceedings{10.1007/978-3-030-86520-7_34,
author="Sadeghi, Afshin
and Collarana, Diego
and Graux, Damien
and Lehmann, Jens",
editor="Oliver, Nuria
and P{\'e}rez-Cruz, Fernando
and Kramer, Stefan
and Read, Jesse
and Lozano, Jose A.",
title="Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities",
booktitle="Machine Learning and Knowledge Discovery in Databases. Research Track",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="548--564",
abstract="Knowledge graphs embeddings (KGE) are lately at the center of many artificial intelligence studies due to their applicability for solving downstream tasks, including link prediction and node classification. However, most Knowledge Graph embedding models encode, into the vector space, only the local graph structure of an entity, i.e., information of the 1-hop neighborhood. Capturing not only local graph structure but global features of entities are crucial for prediction tasks on Knowledge Graphs. This work proposes a novel KGE method named Graph Feature Attentive Neural Network (GFA-NN) that computes graphical features of entities. As a consequence, the resulting embeddings are attentive to two types of global network features. First, nodes' relative centrality is based on the observation that some of the entities are more ``prominent'' than the others. Second, the relative position of entities in the graph. GFA-NN computes several centrality values per entity, generates a random set of reference nodes' entities, and computes a given entity's shortest path to each entity in the reference set. It then learns this information through optimization of objectives specified on each of these features. We investigate GFA-NN on several link prediction benchmarks in the inductive and transductive setting and show that GFA-NN achieves on-par or better results than state-of-the-art KGE solutions.",
isbn="978-3-030-86520-7"
}
```

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