Awesome
New: This project is merged to (https://github.com/thunlp/openke).
==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]