A package for training and evaluating knowledge graph embeddings
Project description
PyKEEN (Python KnowlEdge EmbeddiNgs) is a package for training and evaluating knowledge graph embeddings. Currently, it provides implementations of 10 knowledge graph emebddings models, and can be run in training mode in which users provide their own set of hyper-parameter values, or in hyper-parameter optimization mode to find suitable hyper-parameter values from set of user defined values. PyKEEN can also be run without having experience in programing by using its interactive command line interface that can be started with the command pykeen from a terminal.
Installation 
pykeen can be installed on any system running Python 3.6+ with the following command:
pip install pykeenAlternatively, it can be installed from the source for development with:
$ git clone https://github.com/SmartDataAnalytics/PyKEEN.git pykeen
$ cd pykeen
$ pip install -e .However, GPU acceleration is limited to Linux systems with the appropriate graphics cards as described in the PyTorch documentation.
Installing Extras with Pip
PyKEEN uses pip’s extras functionality to allow some non-essential features to be skipped. They can be installed with the following:
pip install pykeen[ndex] enables support for loading networks from NDEx. They can be added to the training file paths by prefixing files with ndex:
Tutorials
Code examples can be found in the notebooks directory.
Further tutorials are available in our documentation.
CLI Usage - Set Up Your Experiment within 60 seconds
To start the PyKEEN CLI, run the following command:
pykeenthen the command line interface will assist you to configure your experiments.
To start PyKEEN with an existing configuration file, run:
pykeen -c /path/to/config.jsonthen the command line interface won’t be called, instead the pipeline will be started immediately.
Starting the Prediction Pipeline
To make prediction based on a trained model, run:
pykeen-predict -m /path/to/model/directory -d /path/to/data/directorywhere the value for the argument -m is the directory containing the model, in more detail following files must be contained in the directory:
configuration.json
entities_to_embeddings.json
relations_to_embeddings.json
trained_model.pkl
These files are created automatically created after model is trained (and evaluated) and exported in your specified output directory.
The value for the argument -d is the directory containing the data for which inference should be applied, and it needs to contain following files:
entities.tsv
relations.tsv
where entities.tsv contains all entities of interest, and relations.tsv all relations. Both files should contain should contain a single column containing all the entities/relations. Based on these files, PyKEEN will create all triple permutations, and computes the predictions for them, and saves them in data directory in predictions.tsv. Note: the model- and the data-directory can be the same directory as long as all required files are provided.
Optionally, a set of triples can be provided that should be exluded from the prediction, e.g. all the triples contained in the training set:
pykeen-predict -m /path/to/model/directory -d /path/to/data/directory -t /path/to/triples.tsvHence, it is easily possible to compute plausibility scores for all triples that are not contained in the training set.
Summarize the Results of All Experiments
To summarize the results of all experiments, please provide the path to parent directory containing all the experiments as sub-directories, and the path to the output file:
pykeen-summarize -d /path/to/experiments/directory -o /path/to/output/file.csvRelease history Release notifications | RSS feed
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