➜ KB2E_111 git:(master) ag normal | grep distributio
TransH/Train_TransH.cpp:16://normal distribution
TransR/Train_TransR.cpp:18://normal distribution
TransE/Train_TransE.cpp:18://normal distribution
CTransR/Train_CTransR.cpp:17://normal distribution
PTransE/PTransE_add/Train_TransE_path.cpp:24://normal distribution
PTransE/PTransE_RNN/Train_TransE_path.cpp:24://normal distribution
PTransE/PTransE_mul/Train_TransE_path.cpp:24://normal distribution
➜ KB2E_111 git:(master)
➜ KB2E_111 git:(master) tree
.
├── CTransR
│ ├── Test_CTransR.cpp
│ ├── Train_CTransR.cpp
│ └── makefile
├── LICENSE
├── PTransE
│ ├── PCRA.py
│ ├── PTransE_RNN
│ │ ├── Test_TransE_path.cpp
│ │ ├── Train_TransE_path.cpp
│ │ └── makefile
│ ├── PTransE_add
│ │ ├── Test_TransE_path.cpp
│ │ ├── Train_TransE_path.cpp
│ │ └── makefile
│ ├── PTransE_mul
│ │ ├── Test_TransE_path.cpp
│ │ ├── Train_TransE_path.cpp
│ │ └── makefile
│ ├── README.md
│ ├── data
│ │ └── n2n.txt
│ └── makefile
├── README.md
├── TransE
│ ├── Test_TransE.cpp ##
│ ├── Train_TransE.cpp
│ └── makefile
├── TransH
│ ├── Train_TransH.cpp ##
│ └── makefile
├── TransR
│ ├── Test_TransR.cpp
│ ├── Train_TransR.cpp ##
│ └── makefile
├── cluster
│ ├── apcluster.cpp
│ ├── generate.cpp
│ ├── makefile
│ ├── prepare.cpp
│ └── run.sh
├── data.zip
└── makefile
10 directories, 33 files
➜ KB2E_111 git:(master)
New: Add PTransE (EMNLP 2015) code!
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 |
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]
The codes are in the folder TransE/, TransR/, CTransR/.
Just type make in the folder ./
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
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.
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]