kgx 1.5.4

A Python library and set of command line utilities for exchanging Knowledge Graphs (KGs) that conform to or are aligned to the Biolink Model.

Knowledge Graph Exchange

KGX (Knowledge Graph Exchange) is a Python library and set of command line utilities for exchanging Knowledge Graphs (KGs) that conform to or are aligned to the Biolink Model.

The core datamodel is a Property Graph (PG), represented internally in Python using a networkx MultiDiGraph model.

KGX allows conversion to and from:

• RDF serializations (read/write) and SPARQL endpoints (read)
• Neo4j endpoints (read) or Neo4j dumps (write)
• CSV/TSV and JSON (see associated data formats and example script to load CSV/TSV to Neo4j)
• Reasoner Standard API format
• OBOGraph JSON format

KGX will also provide validation, to ensure the KGs are conformant to the Biolink Model: making sure nodes are categorized using Biolink classes, edges are labeled using valid Biolink relationship types, and valid properties are used.

Internal representation is a property graph, specifically a networkx MultiDiGraph.

The structure of this graph is expected to conform to the Biolink Model standard, as specified in the KGX format specification.

In addition to the main code-base, KGX also provides a series of command line operations.

Installation

The installation for KGX requires Python 3.7 or greater.

Installation for users

Installing from PyPI

KGX is available on PyPI and can be installed using pip as follows,

pip install kgx

To install a particular version of KGX, be sure to specify the version number,

pip install kgx==0.5.0

Installing from GitHub

Clone the GitHub repository and then install,

git clone https://github.com/biolink/kgx
cd kgx
python setup.py install

Installation for developers

Setting up a development environment

To build directly from source, first clone the GitHub repository,

git clone https://github.com/biolink/kgx
cd kgx

Then install the necessary dependencies listed in requirements.txt,

pip3 install -r requirements.txt

For convenience, make use of the venv module in Python3 to create a lightweight virtual environment,

python3 -m venv env
source env/bin/activate

pip install -r requirements.txt

To install KGX you can do one of the following,

pip install .

# OR 

python setup.py install

Setting up a testing environment

KGX has a suite of tests that rely on Docker containers to run Neo4j specific tests.

To set up the required containers, first install Docker on your local machine.

Once Docker is up and running, run the following commands:

docker run -d --name kgx-neo4j-integration-test \
            -p 7474:7474 -p 7687:7687 \
            --env NEO4J_AUTH=neo4j/test  \
            neo4j:3.5.25

docker run -d --name kgx-neo4j-unit-test  \
            -p 8484:7474 -p 8888:7687 \
            --env NEO4J_AUTH=neo4j/test \
            neo4j:3.5.25

Note: Setting up the Neo4j container is optional. If there is no container set up then the tests that rely on them are skipped.