Awesome
TransAt:Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism
TransAt is a translation based embedding model for Knowledge Graph Completion. It implements the algorithm of our IJCAI2018 paper: Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism
Benchmark datasets
Datasets are required in the folder data/ in the following format, containing five 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.
Link prediction performance on WN18
| Model | MeanRank(Raw) | MeanRank(Filter) | Hit@10(Raw) | Hit@10(Filter) |
|---|---|---|---|---|
| TransE | 263 | 251 | 75.4 | 89.2 |
| TransH(unif/bern) | 318/401 | 303/388 | 75.4/73.0 | 86.7/82.3 |
| TransR(unif/bern) | 232/238 | 219/225 | 78.3/79.8 | 91.7/92.0 |
| CTransR (unif/bern) | 243/231 | 230/218 | 78.9/79.4 | 92.3/92.3 |
| TransD (unif/bern) | 242/224 | 229/212 | 79.2/79.6 | 92.5/92.2 |
| TranSparse (share, S, unif/bern) | 248/237 | 236/224 | 79.7/80.4 | 93.5/93.6 |
| TranSparse (share, US, unif/bern) | 242/233 | 229/221 | 79.8/80.5 | 93.7/93.9 |
| TranSparse (separate, S, unif/bern) | 235/224 | 223/221 | 79.0/79.8 | 92.3/92.8 |
| TranSparse (separate, US, unif/bern) | 233/223 | 221/211 | 79.6/80.1 | 93.4/93.2 |
| TransAt (bern) | 214 | 202 | 81.4 | 95.1 |
| TransAt (asy,bern) | 169 | 157 | 81.4 | 95.0 |
How to use (require tensorflow 1.1.0 and python 2.7 with numpy, sklearn, cPickle)
train on WN18:
- change "phase" variable in conf/TransAll_v1_WN18.cfg to be "train".
- run "./scripts/TransAll_v1/TransAll_v1_WN18.sh" test on WN18:
- change "phase" variable in conf/TransAll_v1_WN18.cfg to be "test".
- run "./scripts/TransAll_v1/TransAll_v1_WN18.sh"
Reference
Reference to cite when you use TransAt in a research paper
@inproceedings{qian2018translating,
title={Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism.},
author={Qian, Wei and Fu, Cong and Zhu, Yu and Cai, Deng and He, Xiaofei},
booktitle={IJCAI},
pages={4286--4292},
year={2018}