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Knowledge Graph Hub concept

A Knowledge Graph Hub (KG Hub) is framework to download and transform data to a central location for building knowledge graphs (KGs) from different combination of data sources, in an automated, YAML-driven way. The workflow constitutes of 3 steps,

• Download data
• Transform data for each data source into two TSV files (edges.tsv and nodes.tsv) as specified here
• Merge the graphs for each data source of interest using KGX to produce a merged knowledge graph

To facilitate interoperability of datasets, Biolink categories are added to nodes and Biolink association types are added to edges during transformation.

KG-COVID-19 project

The KG-COVID-19 project is the first instantiation of such a KG Hub. Thus, KG-COVID-19 is a framework, that follows design patterns of the KG Hub, to download and transform COVID-19/SARS-COV-2 related datasets and emit a knowledge graph that can then be used for machine learning or others uses, to produce actionable knowledge.

The codebase

• Here is the GitHub repo for this project.
• Here is the GitHub repo for Embiggen, an implementation of node2vec and other methods to generate embeddings and apply machine learning to graphs.
• Here is the GitHub repo for KGX, a knowledge graph exchange tool for working with graphs

Installation

    git clone https://github.com/Knowledge-Graph-Hub/kg-covid-19
    cd kg-covid-19
    pip install -r requirements.txt
    python setup.py install

Running the pipeline

    python run.py download
    python run.py transform
    python run.py merge

A few organizing principles used for data ingest

• UniProtKB identifiers are used for genes and proteins, where possible
• For drug/compound identifiers, there is a preferred namespace. If there are datasets that provide identifiers from multiple namespaces then the choice is determined based on a descending order of preference,
  • CHEBI > CHEMBL > DRUGBANK > PUBCHEM
• Less is more: for each data source, we ingest only the subset of data that is most relevant to the knowledge graph in question (here, it's KG-COVID-19)
• We avoid ingesting data from a source that isn't authoritative for the data in question (e.g. we do not ingest protein interaction data from a drug database)
• Each ingest should make an effort to add provenance data by adding a provided_by column for each node and edge in the output TSV file, populated with the source of each datum

Download KG-COVID-19 Knowledge Graph

Our merged knowledge graph comprises of data from all available transforms and is available in one of the following serialization formats:

• RDF N-Triples
• KGX TSV
  • See here for a description of the KGX TSV format

Querying the graph

A SPARQL endpoint for the merged knowledge graph is available here. For a better experience, consider using https://yasgui.triply.cc/ for your querying needs. If you are not sure where to start, here are some example SPARQL queries: https://github.com/Knowledge-Graph-Hub/kg-covid-19/tree/master/queries/sparql

Summary of the data

A detailed, up-to-date summary of data in KG-COVID-19 is available here, with contents of the knowledge graph broken down by Biolink categories and Biolink association types for nodes and edges, respectively.

An interactive dashboard to explore these stats is available here.

A schematic qualitative summary of data in our graph is given below,

How to Contribute

• Here is a more detailed description, and instructions on how to help us with our KG-COVID-19 effort.

Contributors

• Justin Reese
• Deepak Unni
• Marcin Joachimiak
• Peter Robinson
• Chris Mungall
• Tiffany Callahan
• Luca Cappelletti
• Vida Ravanmehr

Acknowledgements

We gratefully acknowledge the Elsevier Coronavirus Information Center for sharing their coronavirus pathway data, and also acknowledge and thank all COVID-19 data providers for making their data available.