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EMNLP 2018: HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
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README.md

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_type yago -margin 10 -model 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"
}
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