===== DATA =====
The data use in the experiment can download in:
FB15k, WN18 are published by the author of the paper "Translating Embeddings for Modeling Multi-relational Data (2013)."
FB13, WN11 are published by the author of the paper "Reasoning With Neural Tensor Networks for Knowledge Base Completion".
Datasets are needed in the folder data/ in the following format
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.
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entity2id.txt: all entities and corresponding ids, one per line.
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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/
TransH: call the program Train_TransH in folder TransH/
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:
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
==CITE==
If you use the code, you should cite the following paper:
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. The 29th AAAI Conference on Artificial Intelligence (AAAI'15).[pdf]