GAKE: Graph Aware Knowledge Embedding(COLING2016)
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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".


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


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


Just type "make" in the corresponding folder.


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


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]


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