TransR

TransR[AAAI 2015]

• paper: Learning Entity and Relation Embeddings for Knowledge Graph Completion

Basic Idea

• Entities are embedded in a continuous low dimensional vector space and each relations are embedded in different vector space.
• Judge whether triplet (entities, relationships, entities) can be considered as a fact by similarity based on distance.
• First, the entity vector is projected onto the vector space related to the relation, and then use L2 normal form as the similarity calculation method, and the formula is as follows:

$$ f(\textbf{h},\textbf{r},\textbf{t}) = | \textbf{M}{r}\textbf{h}+\textbf{r}-\textbf{M}{r}\textbf{t}|_{2}^{2} $$

• The negative samples are constructed by destroying the fact triples to train the model.

$$ S' = \left { (h',r,t) \right |h'\in E } \cup \left { (h,r,t') \right |t'\in E } $$

• The loss calculation method is as follows:

$$ Loss = \sum_{(\textbf{h},\textbf{r},\textbf{t}) \in S}\sum_{(\textbf{h}',\textbf{r},\textbf{t}') \in S'}[\gamma - f(\textbf{h},\textbf{r},\textbf{t}) + f(\textbf{h}',\textbf{r},\textbf{t}')]_{+} $$

• Finally, use BP to update the model.

• In addition, CTransR uses the results of TransE to pre cluster entities, and then learns a relationship vector $ \textbf{r}_{c} $ for each cluster. The new loss is as follows:

$$ f_{r}(h,t)=| \textbf{M}{r}\textbf{h}+\textbf{r}-\textbf{M}{r}\textbf{t} |{2}^{2} + \alpha | \textbf{r}{c}-\textbf{r} |_{2}^{2} $$

• What`s more, the dimensions of the entity embedding vector and the relationship embedding vector can be different.

How to run

• Clone the Openhgnn-DGL

• Run transE model first

  # For link prediction task
  python main.py -m TransE -t link_prediction -d FB15k -g 0 --use_best_config

• Run transR model

  # For link prediction task
  python main.py -m TransR -t link_prediction -d FB15k -g 0 --use_best_config

  If you do not have gpu, set -gpu -1.

  Supported dataset

  • FB15k

    • Number of entities and relations

      entities	relations
      14,951	1,345

    • Size of dataset

      set type	size
      train set	483,142
      validation set	50,000
      test set	59,071

  • WN18

    • Number of entities and relations

      entities	relations
      40,493	18

    • Size of dataset

      set type	size
      train set	141,442
      validation set	5,000
      test set	5,000

Performance

Task: Link Prediction

Evaluation metric: mrr

Testing model performance...

TrainerFlow: TransX flow

Hyper-parameter specific to the model

You can modify the parameters[TransE] in openhgnn/config.ini

More

Contirbutor

Xiaoke Yang

If you have any questions

Submit an issue or email to [email protected].