zhichun / TransEA

An implementation of TransEA for knowledge representation learning and knowledge graph completion.

TransEA [1]

This is an implementation of the paper Knowledge Graph Embedding with Numeric Attributes of Entities .

These codes base on TransE [2] and TensorFlow-TransX

Data

Datasets are required in the following format:

• triple2id.txt: training file, the first line is the number of triples for training. Then the follow lines are all in the format (e1, e2, rel).

• valid2id.txt: the same format as triple2id.txt

• test2id.txt: the same format as triple2id.txt

• entity2id.txt: all entities and corresponding ids, one per line. The first line is the number of entities.

• relation2id.txt: all relations and corresponding ids, one per line. The first line is the number of relations.

• attr2id.txt: all numeric attributes and corresponding ids. The first line is the number of attribute relation.

• attrm2id.txt: attributive triplets, the first line is the number of attributive triplets. Format (e, a, v), v is normalized by Max-Min method.

Compile

bash makeEA.sh

Train

To train models based on random initialization:

1. Change class Config in transX.py

    class Config(object):
   
    	def __init__(self):
    		...
    		lib.setInPath("your training data path...")
    		self.testFlag = False
    		self.loadFromData = False
    		...
   

2. python transE/transEA.py

Test

To test your models:

1. Change class Config in transX.py

    class Config(object):
   
    	def __init__(self):
    		...
    		test_lib.setInPath("your testing data path...")
    		self.testFlag = True
    		self.loadFromData = True
    		...
   

2. python transE/transEA.py

Citation

If you use the code or datasets, please kindly cite the papers listed in our reference.

Reference

[1] Yanrong Wu, Zhichun Wang. Knowledge Graph Embedding with Numeric Attributes of Entities. Proceedings of The Third Workshop on Representation Learning for NLP. 2018.

[2] Bordes, Antoine, et al. Translating embeddings for modeling multi-relational data. Proceedings of NIPS, 2013.

[3] Yankai Lin, Zhiyuan Liu, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion. Proceedings of AAAI, 2015.