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Knowledge Graph Hub concept

A Knowledge Graph Hub (KG Hub) is software 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 is:

• 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 associations are added to edges during transformation.

KG-COVID-19 project

The KG-Covid-19 project is the first such KG Hub. Output is a Knowledge Graph Hub that downloads and transforms COVID-19/SARS-COV-2 and related data and emits a knowledge graph that can be loaded into KGX and used for machine learning or others uses, to produce actionable knowledge.

Download knowledge graph:

A merged knowledge graph comprised of data from all available transforms is here:

RDF

TSV

See here for a description of the KGX TSV format.

Summary of data (Apr 2020):

A detailed, up-to-date summary of data in kg-covid-19 is here, with contents of the knowledge graph broken down by biolink categories and biolink associations for nodes and edges, respectively.

As of July 6, 2020, the graph contains the following data:

  count_by_category:
    biolink:OntologyClass: 80005
    biolink:Publication: 52105
    biolink:Drug: 32204
    biolink:BiologicalProcess: 30702
    biolink:ChemicalSubstance: 29858
    biolink:Protein: 21070
    biolink:Gene: 19240
    biolink:MolecularActivity: 12202
    human_phenotype: 10384
    biolink:AnatomicalEntity: 4656
    biolink:CellularComponent: 4454
    unknown: 4340
    biolink:Disease: 289
    biolink:NamedThing: 227
    external: 87
    biolink:Assay: 48
    sequence: 46
    biolink:RNA: 7
    biolink:OrganismalEntity: 6
    biolink:MolecularEntity: 1
    gene_ontology: 1

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.

A few organizing principles:

• UniprotKB IDs are used for genes and proteins when possible
• For drug/compound IDs, these IDs are preferred, in 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 KG-Hub in question (here KG-COVID-19)
• We avoid ingesting data from a source that isn't authoritative for the data in question (e.g. 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 in each edge TSV file, populated with the source of each datum

People:

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

The code:

• 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.

Installation:

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

Running the code:

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

Querying the graph:

A SPARQL endpoint for the complete, merged graph with all available source data is here. Consider using https://yasgui.triply.cc/ for querying. Here are some example queries: https://github.com/Knowledge-Graph-Hub/kg-covid-19/tree/master/queries/sparql

Contributing:

• Here is a more detailed description, and instructions on how to help.