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Knowledge Graph Embeddings including TransE, TransH, TransR and PTransE

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===== DATA =====

Datasets are included in the folder data/.

Dataset contains six files:

  • train.txt: training file, format (e1, e2, rel).

  • valid.txt: validation file, same format as train.txt

  • test.txt: test file, same format as train.txt.

  • entity2id.txt: all entities and corresponding ids, one per line.

  • relation2id.txt: all relations and corresponding ids, one per line.

Currently we cannot upload data due to huge size. We will release data with codes together once the paper is published.

===== CODE =====

In the folder TransE/, TransR/, CTransR/:

===== COMPILE =====

Just type make in the folder ./

== TRAINING ==

For training, You need follow the step below:

TransE:

call the program Train_TransE in folder TransE/

TransR:

1:	Train the unif method of TransE as initialization.

2:  call the program Train_TransR in folder TransR/

CTransR:

1:	Train the unif method of TransR as initialization.

2:  run the bash run.sh with relation number in folder cluster/ to cluster the triples in the trainning data.

	i.e.

		bash run.sh 10

3:  call the program Train_cTransR in folder CTransR/

You can also change the parameters when running Train_TransE, Train_TransR, Train_CTransR.

-size : the embedding size k, d

-rate : learing rate

-method: 0 - unif, 1 - bern

== TESTING ==

For testing, You need follow the step below:

TransE:

call the program Test_TransE with method as parameter in folder TransE/

i.e.

	Test_TransE unif

	Test_TransE bern

TransR:

call the program Train_TransR with method as parameter in folder TransR/

CTransR:

call the program Train_CTransR with method as parameter in folder CTransR/

It will evaluate on test.txt and report mean rank and Hits@10..

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  • C++ 95.5%
  • Python 3.3%
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