1. README

An Open-source library for Knowledge Embedding forked from github.org/thunlp/OpenKE. The original API changed drastically to be more pythonic.

1.1. Overview

This is an implementation based on [TensorFlow](http://www.tensorflow.org) for knowledge representation learning (KRL). It includes native C++ implementations for underlying operations such as data preprocessing and negative sampling. For each specific model, it is implemented by TensorFlow with Python interfaces so that there is a convenient platform to run models on GPUs.

1.2. Installation

  1. Clone repository and enter directory

    git clone https://github.com/ifis-tu-bs/pyKE.git
cd pyKE

  2. Install package

    python setup.py install

1.3. Quickstart

To compute a knowledge graph embedding, first import datasets and set configure parameters for training, then train models and export results. Here is an example to train the FB15K dataset with the TransE model.

from pyke.dataset import Dataset
from pyke.embedding import Embedding
from pyke.models import TransE

# Read the dataset
dataset = Dataset("./benchmarks/fb15k.nt")
embedding = Embedding(
    dataset,
    TransE,
    folds=20,
    epochs=20,
    neg_ent=1,
    neg_rel=0,
    bern=False,
    workers=4,
    dimension=50,  # TransE-specific
    margin=1.0,  # TransE-specific
)

# Train the model. It is saved in the process.
embedding.train(prefix="./TransE", post_epoch=print)

# Save the embedding to a JSON file
embedding.save_to_json("TransE.json")

1.4. Interfaces

The class pyke.embedding.Embedding represents an embedding which requires a dataset and a model class. Initialize your data set in form of a N-triples file with the class pyke.dataset.Dataset.

1.4.1. Models

The class pyke.models.base.BaseModel declares the methods that all implemented model classes share, including the loss function neccessairy for training (inserting information into the model) and prediction (aka. retrieving information from the model). This project implements the following model classes:

  • RESCAL
  • TransE
  • TransH
  • TransR
  • TransD
  • HolE
  • ComplEx
  • DistMult

1.5. Notes

The original fork consists of a C++ library which is compiled once you use the project. Please note, that the compilation is only supported on UNIX-based systems. In the future the C++ library should be replaced by a python library.