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# TransE Experiments This fork of the repository containing the original code for [the TransE knowledge graph embedding model](https://everest.hds.utc.fr/lib/exe/fetch.php?media=en:cr_paper_nips13.pdf), provides some additional scripts that make data processing, model training, and hyperparamter optimisation more convinient. The original README can be found below. ## Download and Process Data The Freebase and WordNet datasets are not included in the project, so the raw data has to be downloaded and processed into the WN18 and FB15K subsets. This is automated through two Bash scripts that you run in sequence from the project root: * `./download_data.sh` downloads the raw Freebase and WordNet datasets. * `./process_data.sh` initiates the processing procedures by running the `{dataset}/{dataset}_parse.py` Python script in the FB15K and WN folders. ## Training The `{dataset}/{dataset}_TransE.py` Python scripts have been extended with a command line interface that allows the user to specify hyperparameters. Furthermore, the `train.sh` Bash script acts as a wrapper around those scripts and sets the `PYTHONPATH` environment variable properly. It is possible to provide hyperparamtere configurations in two different ways: * Specify a path to a JSON file containing the parametere values by passing the `--config` argument. For instance, `./train.sh fb --config path/to/config.json` * Specify each parameter value as an argument to the `train.sh` script. For instance, `./train.sh wn --epochs 10 --dimensions 100` ## Hyperparameter Grid Search The code does not provide a direct way to conduct a hyperparameter search. Instead, the `grid_search.py` Python script can generate a collection of hyperparameter configuration files along with Bash scripts to run the training procedure. --- SME === The architecture of this package has been designed by **Xavier Glorot** (https://github.com/glorotxa), with some contributions from **Antoine Bordes** (https://www.hds.utc.fr/~bordesan). **Update (Nov 13):** the code for Translating Embeddings (see https://everest.hds.utc.fr/doku.php?id=en:transe) has been included along with a new version for Freebase (FB15k). 1. Overview ----------------------------------------------------------------- This package proposes scripts using Theano to perform training and evaluation on several datasets of the models: - **Structured Embeddings** (SE) defined in (Bordes et al., AAAI 2011); - **Semantic Matching Energy** (SME_lin & SME_bil) defined in (Bordes et al., MLJ 2013); - **Translating Embeddings** (TransE) defined in (Bordes et al., NIPS 2013). - **TATEC** defined in (Garcia-Duran et al., ECML14, arxiv15). Please refer to the following pages for more details and references: - https://everest.hds.utc.fr/doku.php?id=en:smemlj12 - https://everest.hds.utc.fr/doku.php?id=en:transe - https://everest.hds.utc.fr/doku.php?id=en:2and3ways Content of the package: - model.py : contains the classes and functions to create the different models and Theano functions (training, evaluation...). - {dataset}_exp.py : contains an experiment function to train all the different models on a given dataset. - The data/ folder contains the data files for the learning scripts. - in the {dataset}/ folders: * {dataset}_parse.py : parses and creates data files for the training script of a given dataset. * {dataset}_evaluation.py : contains evaluation functions for a given dataset. * {dataset}\_{model_name}.py : runs the best hyperparameters experiment for a given dataset and a given model. * {dataset}\_{model_name}.out : output we obtained on our machines for a given dataset and a given model using the script above. * {dataset}_test.py : perform quick runs for small models of all types to test the scripts. The datasets currently available are: * **Multi-relational benchmarks** (Kinhsips, UMLS & Nations -- Tensor folder) to be downloaded from https://everest.hds.utc.fr/doku.php?id=en:smemlj12 * **WordNet** (WN folder) to be downloaded from https://everest.hds.utc.fr/doku.php?id=en:smemlj12 * **Freebase** (FB folder) used in (Bordes et al., AAAI 2011) to be downloaded from https://everest.hds.utc.fr/doku.php?id=en:smemlj12 * **Freebase15k** (FB15k folder) used in (Bordes et al., NIPS 2013) to be downloaded from https://everest.hds.utc.fr/doku.php?id=en:transe * **Synthethic family database** (Family folder) user is (Garcia-Duran et al. arxiv15a) to be downloaded from https://everest.hds.utc.fr/doku.php?id=en:2and3ways 2. 3rd Party Libraries ----------------------------------------------------------------- You need to install Theano to use those scripts. It also requires: Python >= 2.4, Numpy >=1.5.0, Scipy>=0.8. The experiment scripts are compatible with Jobman but this library is not mandatory. 3. Installation ----------------------------------------------------------------- Put the script folder in your PYTHONPATH. 4. Data Files Creation ----------------------------------------------------------------- Put the absolute path of the downloaded dataset (from: https://everest.hds.utc.fr/doku.php?id=en:smemlj12 or https://everest.hds.utc.fr/doku.php?id=en:transe) at the beginning of the {dataset}_parse.py script and run it (the SME folder has to be your current directory). Note: Running Tensor_parse.py generates data for both Kinhsips, UMLS & Nations. 5. Training and Evaluating a Model ----------------------------------------------------------------- Simply run the corresponding {dataset}_{model_name}.py file (the SME/{dataset}/ folder has to be your current directory) to launch a training. When it's over, running {dataset}_evaluation.py with the path to the best_valid_model.pkl of the learned model runs the evaluation on the test set 6. Citing ----------------------------------------------------------------- If you use this code, you could provide the link to the github page: https://github.com/glorotxa/SME . Also, depending on the model used, you should cite either the paper on **Structured Embeddings** (Bordes et al., AAAI 2011), on **Semantic Matching Energy** (Bordes et al., MLJ 2013) or on **Translating Embeddings** (Bordes et al., NIPS 2013). 7. References ----------------------------------------------------------------- - (Garcia-Duran et al., arxiv 15) *Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases* Alberto Garcia-Duran, Antoine Bordes, Nicolas Usunier and Yves Grandvalet. http://arxiv.org/abs/1506.00999 - (Bordes et al., NIPS 2013) *Translating Embeddings for Modeling Multi-relational Data* (2013). Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston and Oksana Yakhnenko. In Proceedings of Neural Information Processing Systems (NIPS 26), Lake Taho, NV, USA. Dec. 2013. - (Bordes et al., MLJ 2013) *A Semantic Matching Energy Function for Learning with Multi-relational Data* (2013). Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. in Machine Learning. Springer, DOI: 10.1007/s10994-013-5363-6, May 2013 - (Bordes et al., AAAI 2011) *Learning Structured Embeddings of Knowledge Bases* (2011). Antoine Bordes, Jason Weston, Ronan Collobert and Yoshua Bengio. in Proceedings of the 25th Conference on Artificial Intelligence (AAAI), AAAI Press. # EsnarT-Test

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