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The project will no longer be maintained and users are recommended to access and use the new package https://github.com/thunlp/OpenKE.

Evaluation Results

We list the result of various methods implemented by ourselves in dateset FB15k and WN18.

FB15k

Model MeanRank(Raw) MeanRank(Filter) Hit@10(Raw) Hit@10(Filter)
TransE(paper) 243 125 34.9 47.1
TransH(paper) 212 87 45.7 64.4
TransR(n=50) 198 77 48.2 68.7
TransE(Our, n=50) 210 82 41.9 61.3
TransE(Our, n=100) 205 63 47.9 70.2
PTransE (ADD, 2-step) 200 54 51.8 83.4
PTransE (MUL, 2-step) 216 67 47.4 77.7
PTransE (RNN, 2-step) 242 92 50.6 82.2
PTransE (ADD, 3-step) 207 58 51.4 84.6

WN18

Model MeanRank(Raw) MeanRank(Filter) Hit@10(Raw) Hit@10(Filter)
TransE(paper) 263 251 75.4 89.2
TransH(paper) 318 303 75.4 86.7
TransR 238 225 79.8 92.0
TransE(Our) 251 239 78.9 89.8

Data

We provide FB15k and WN18 datasets used for the task link prediction in data.zip, using the input format required by our codes. The original data can be downloaded from:

FB15k, WN18 are published by "Translating Embeddings for Modeling Multi-relational Data (2013)." [Download]

FB13, WN11 are published by "Reasoning With Neural Tensor Networks for Knowledge Base Completion". [Download]

New York Times Corpus: The data used in relation extraction from text is publish by "Modeling relations and their mentions without labeled text". The data should be obtained from LDC (https://catalog.ldc.upenn.edu/LDC2008T19) first.

FB40k [Download]

Datasets are required in the folder data/ in the following format, containing 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.

  • e1_e2.txt: the top-500 entity pairs which are calculated by TransE. [Download]

Code

The codes are in the folder TransE/, TransR/, CTransR/.

Compile

Just type make in the folder ./

Training

For training, you need to follow the steps below:

TransE: call the program Train_TransE in folder TransE/

TransH: call the program Train_TransH in folder TransH/

TransR:

  • Train the unif method of TransE as initialization.

  • Call the program Train_TransR in folder TransR/

CTransR:

  • Train the unif method of TransR as initialization.

  • Run the bash run.sh with relation number in folder cluster/ to cluster triples in the trainning data. e.g., bash run.sh 10

  • 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 to follow the steps below:

TransR: Call the program Test_TransR with method as parameter in folder TransR/

CTransR: Call the program Test_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, please kindly 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]