I thought I’d have a look at a few different things this week as the title suggests: graph embeddings and named entity recognition through the medium of Sports Science research papers. So strap yourself in and let’s dive in…

Welcome back and happy new year to all four (at last count) of my regular readers. We’ll continue where we left off; creating a GraphQL endpoint with Neo4j . This week we’ll go deeper and look at how we can call an API from within the GraphQL endpoint to enrich our data.

Well, I think it’s only right we start with an apology for the post title. Sorry. However, it will most definitely happen again. So not that sorry I guess.

Inspired by a recent uneventful stint on jury service, I started wading through the open government datasets just out of curiosity. Maybe I was hoping to unearth the next set of Panama Papers, or maybe there is just alot of waiting around during jury service. Either way, one subset of these datasets piqued my interest: Organograms (which according to Google is just another term for an organization chart).

Following on from last weeks post in which we loaded a graph using Bio2RDF, this week we’ll look at some of the issues in parsing the data and then querying the graph.

When exploring some linked datasets I stumbled across Bio2RDF, a resource for linked data for life sciences. This got me thinking how I could link this into WADA’s prohibited list and examine some of the genes linked to these Performance Enhancing Drugs (PED’s).