Knowledge Graph Embedding Server’s documentation


You can skip this introduction if you know all about Knowledge graphs or you don’t want to learn about them. Just go down to check how to install or run the service.

What are Knowledge Graphs?

There are many knowledge databases nowadays, and they are growing very quickly. Some of them are open and have a very broad domain, like [DBpedia]( or [Wikidata](, both based in existent data on Wikipedia. Other knowledge databases are based on very specific domains, like [](, which stores the information from Spanish National Library (Biblioteca Nacional de España) in an open, machine readable, way.

Most of those knowledge databases can be seen as knowledge graphs, where facts can be seen as triples: head, label and tail. This information is usually stored using semantic web tools, like RDF and can be queried through some languages like SPARQL.

What are Embeddings?

Embeddings are a way to represent all the relationships that exists on graphs, and they are commonly represented as multidimensional arrays. Those are useful to perform some machine learning tasks such as look for similar entities. With some embeddings models you can also do some simple algebraic operations with those arrays like adding them or substract and predict new entities.

What is this server?

This server provides a vertical solution on the machine learning area, going from the creation of datasets wich represents those knowledge graphs, to methods to perform queries such as look for similar entities given another. In the middle, the server provides training and indexing models that allows the query operations shown above.

What is included here?

The vertical solution depicted above is available as a Python library, so you can do a python3 install and that’s all. But you can also deploy a web service using docker that is able to do almost every of those operations through a HTTP client. You can take a look to the documentation and discover all the things you can do on the Table of contents.


You can use the Python library as is, or you can start a server, and use all the endpoints available.

Library installation

This repository provides a setuptools file to install the library on your system. It is pretty easy. Simply make sudo python3 install and it will install the library. Maybe some extra dependencies are required to run into your system, if so, you can execute this to get them all installed:

conda install scikit-learn scipy cython

And if you are using normal python3:

pip3 install numpy scipy pandas sympy nose scikit-learn

But the recommended way to getthe REST service working is to execute into the docker environment. You only need to have installed docker` and `docker-compose in your system.

Service execution

To run the service, go to images folder, execute docker-compose up and you will have a server on the port localhost:6789 ready to listen HTTP requests.

cd images/
docker-compose up -d
curl http://localhost:6789/datasets

After this you will have an HTTP REST server listening to the API. but if you want to run the python library alone, you can connect to any of the docker containers created:

docker exec -it images_web_1 /bin/bash

If you are experiencing troubles when executing the docker image, check your user UID and change the user UID in all Dockerfiles` inside `images/ folder. Then rebuild the images with: docker-compose build --no-cache

See more instructions about deployment at the Server deployment section.

Supported environment

The whole project has been built using Python 3.5 distributed by Anaconda, inside a docker image. If you want to run the development environment, just use this image recognai/jupyter-scipy-kge.