adofsauron/relation_extraction

Knowledge Base Embedding

New: Add PTransE (EMNLP 2015) code!

==RESULT==

We list the result of the code in date set 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 =====

I provide FB15k and WN18 datasets used for the task link prediction with the input format of my code in data.zip.

The original 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)." [Download]

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

New York Times Corpus: The data used in relation extraction from text which is publish by the paper " Modeling relations and their mentions without labeled text". If you want the data, you should buy from LDC (https://catalog.ldc.upenn.edu/LDC2008T19) first.

FB40k [Download]

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.

• 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/

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 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, 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]