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

• FB15k,WN18
• FB13,WN11

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.

• entity2id.txt: all entities and corresponding ids, one per line.

• 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:

1. change "phase" variable in conf/TransAll_v1_WN18.cfg to be "train".
2. run "./scripts/TransAll_v1/TransAll_v1_WN18.sh" test on WN18:
3. change "phase" variable in conf/TransAll_v1_WN18.cfg to be "test".
4. 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}