HyTE

HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding

Source code and dataset for EMNLP 2018 paper: HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding.

Overview of HyTE (proposed method). a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by stitching each timestamp with a corresponding hyperplane. HyTE not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations. Please refer paper for more details.

Dependencies

Compatible with TensorFlow 1.x and Python 3.x.
Dependencies can be installed using requirements.txt.

Dataset:

Download the processed version of WikiData and YAGO datasets.
Unzip the .zip file in data directory.
Documents are originally taken from YAGO and Wikidata.

Usage:

After installing python dependencies from requirements.txt.
time_proj.py contains TensorFlow (1.x) based implementation of HyTE (proposed method).

To start training:

python time_proj.py -data data/processed_data.pkl -class 10 -name MODEL_NAME -test_freq 25 -<other_optins> ...

Some of the important Available options include:

  '-data_type' default ='yago', choices = ['yago','wiki_data'], help ='dataset to choose'
  '-version',  default = 'large', choices = ['large','small'], help = 'data version to choose'
  '-test_freq', 	 default = 25,   	type=int, 	help='testing frequency'
  '-neg_sample', 	 default = 5,   	type=int, 	help='negative samples for training'
  '-gpu', 	 dest="gpu", 		default='1',			help='GPU to use'
  '-name', 	 dest="name", 		help='Name of the run'
  '-lr',	 dest="lr", 		default=0.0001,  type=float,	help='Learning rate'
  '-margin', 	 dest="margin", 	default=1,   	type=float, 	help='margin'
  '-batch', 	 dest="batch_size", 	default= 50000,   	type=int, 	help='Batch size'
  '-epoch', 	 dest="max_epochs", 	default= 5000,   	type=int, 	help='Max epochs'
  '-l2', 	 dest="l2", 		default=0.0, 	type=float, 	help='L2 regularization'
  '-seed', 	 dest="seed", 		default=1234, 	type=int, 	help='Seed for randomization'
  '-inp_dim',  dest="inp_dim", 	default = 128,   	type=int, 	help='')
  '-L1_flag',  dest="L1_flag", 	action='store_false',   	 	help='Hidden state dimension of FC layer'

Evaluation:

After trainig start validation/test. Use the same model name and test frequency used at training as arguments for the following evalutation--
For getting MR and hit@10 for head and tail prediction:

   python result_eval.py -model MODEL_NAME  -test_freq 25

For getting MR and hit@10 for relation prediction:

   python result_eval_relation.py -model MODEL_NAME  -test_freq 25

Citing:

InProceedings{D18-1225,
  author = 	"Dasgupta, Shib Sankar
		and Ray, Swayambhu Nath
		and Talukdar, Partha",
  title = 	"HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding",
  booktitle = 	"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"2001--2011",
  location = 	"Brussels, Belgium",
  url = 	"http://aclweb.org/anthology/D18-1225"
}

NB:

Source code can also be found at MALL Lab. This is just a copy.

SwayambhuNathRay/HyTE-EMNLP-2018