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