JuneFeng / GAKE

GAKE: Graph Aware Knowledge Embedding(COLING2016)

GAKE:Graph Aware Knoweldge Embedding

Knowledge Graph Embedding projects triples in a given knowledge graph to d-dimensional vectors. We provide the source code and datasets of the COLING 2016 paper: "GAKE:Graph Aware Knowledge Embedding".

DataSets

We provide FB15K in data folder. The data is originally released by the paper "Translating Embeddings for Modeling Multi-relational Data". [Download]

The data format is:

• train.txt: training data, format(entity1, relation, entity2)
• valid.txt: validation data, same format as training data
• test.txt: test data, same format as training data
• entity_to_id.txt: all entities and corresponding ids, one per line
• relation_to_id.txt: all relations and corresponding ids, one per line

Code

We refer to the implement code of CBOW model published by Google.[code]

Compile

Just type "make" in the corresponding folder.

Train

For training, you need to type "./main [dim] [window] [alpha] [loopNum] [attentionLabel] [pathContextNum] [edgeContextNum] [edgeNum] [pathRate] [edgeRate]" in the corresponding folder.

The output of the model will be saved in folder result/.

Parameter Setting:

• dim: the dimension of embedding vectors
• window: the length of path context
• alpha: learning rate
• loopNum: training iteration number
• attentionLabel: use the attention mechanism or not
• pathContextNum: path context number
• edgeContextNum: edge context number
• edgeNum: the number of chosen edges for each entity
• pathRate: the prestige of path context
• edgeRate: the prestige of edge context

Cite

If you use the code, pleasee cite the following paper: [Feng et al. 2016] Jun Feng, Minlie Huang, Yang Yang, and Xiaoyan Zhu. GAKE: Graph Aware Knowledge Embedding. In COLING2016. [pdf]

Reference

[1] [Borders et al. 2013] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran. Translating Embedding for Modeling Multi-Relational Data.

[2] https://code.google.com/archive/p/word2vec/