GraphQL for WikiData (WikiCite)

I've released a very crude GraphQL endpoint for WikiData. More precisely, the endpoint is for a subset of the entities that are of interest to WikiCite, such as scholarly articles, people, and journals. There is a crude demo at https://wikicite-graphql.herokuapp.com. The endpoint itself is at https://wikicite-graphql.herokuapp.com/gql.php. There are various ways to interact with the endpoint, personally I like the Altair GraphQL Client by Samuel Imolorhe.

As I've mentioned earlier it's taken me a while to see the point of GraphQL. But it is clear it is gaining traction in the biodiversity world (see for example the GBIF Hosted Portals) so it's worth exploring. My take on GraphQL is that it is a way to create a self-describing API that someone developing a web site can use without them having to bury themselves in the gory details of how data is internally modelled. For example, WikiData's query interface uses SPARQL, a powerful language that has a steep learning curve (in part because of the administrative overhead brought by RDF namespaces, etc.). In my previous SPARQL-based projects such as Ozymandias and ALEC I have either returned SPARQL results directly (Ozymandias) or formatted SPARQL results as schema.org DataFeeds (equivalent to RSS feeds) (ALEC). Both approaches work, but they are project-specific and if anyone else tried to build based on these projects they might struggle for figure out what was going on. I certainly struggle, and I wrote them!

So it seems worthwhile to explore this approach a little further and see if I can develop a GraphQL interface that can be used to build the sort of rich apps that I want to see. The demo I've created uses SPARQL under the hood to provide responses to the GraphQL queries. So in this sense it's not replacing SPARQL, it's simply providing a (hopefully) simpler overlay on top of SPARQL so that we can retrieve the data we want without having to learn the intricacies of SPARQL, nor how Wikidata models publications and people.

The Business of Extracting Knowledge from Academic Publications

Markus Strasser (@mkstra write a fascinating article entitled "The Business of Extracting Knowledge from Academic Publications".

I spent months working on domain-specific search engines and knowledge discovery apps for biomedicine and eventually figured that synthesizing "insights" or building knowledge graphs by machine-reading the academic literature (papers) is *barely useful* :https://t.co/eciOg30Odc

— Markus Strasser (@mkstra) December 7, 2021

His TL;DR:

TL;DR: I worked on biomedical literature search, discovery and recommender web applications for many months and concluded that extracting, structuring or synthesizing "insights" from academic publications (papers) or building knowledge bases from a domain corpus of literature has negligible value in industry.

Close to nothing of what makes science actually work is published as text on the web.

After recounting the many problems of knowledge extraction - including a swipe at nanopubs which "are ... dead in my view (without admitting it)" - he concludes:

I’ve been flirting with this entire cluster of ideas including open source web annotation, semantic search and semantic web, public knowledge graphs, nano-publications, knowledge maps, interoperable protocols and structured data, serendipitous discovery apps, knowledge organization, communal sense making and academic literature/publishing toolchains for a few years on and off ... nothing of it will go anywhere.

Don’t take that as a challenge. Take it as a red flag and run. Run towards better problems.

Well worth a read, and much food for thought.