The Nationalmuseum API

18th February 2020

Nationalmuseum, the national gallery of Sweden has had an API for about two and a half year (I think) but it has yet to have any public documentation. So I decided to write down its features and some random tips.

Capabilities

The API is able to return specific objects as well as returning all objects through pagination. Many objects also link to their external IIIF manifest.

Retrieving an object

https://api.nationalmuseum.se/api/objects?id=3618

Check.

Pagination through all objects

The API has two parameters for pagination page and limit. limit sets the number of objects you get per page the default is 50 and allowed values range from 1-100.

Get the 100-200 objects:

https://api.nationalmuseum.se/api/objects?page=2&limit=100

The response also returns you the URLs for the previous and next pages taking the limit parameter into account.

Random notes

The URLs to items can seem daunting when you browse Nationalmuseums online collection but there is an alternative that’s easy to link to with the identifier you get from the API:

http://collection.nationalmuseum.se/eMP/eMuseumPlus?service=ExternalInterface&module=collection&viewType=detailView&objectId=24342

A great way to find interesting objects in the collection and connect it to other sources is to ask Wikidata: Objects in Wikidata with Nationalmuseum IDs

Finally for some insperation you could check out the Nationalmuseum VIKUS Viewer instance.

Reading List Week 6 2020

7th February 2020

I haven’t done a weekly reading list on this blog since high school; this week I did for a mysterious unknown reason. Alternative title: Links with random mumblings.

GLAM and Museum Tech

Accessibility

Other

A SPARQL Editor for SOCH

19th January 2020

screenshot of the editor

About a year ago I made a custom SPARQL editor for the Swedish Open Cultural Heritage (SOCH/K-samsök) LOD platform since then it has served me, both at home and at the office. Now I’m hosting an instance for anyone to use. It was built both to aid me (and others) working with the over 8 million RDF records in SOCH as well as to be a prof of concept and reference for future work.

The editor comes with a ton of features including:

There is no official SPARQL endpoint for SOCH so the first time you uses the editor you will be asked to enter your own endpoint or a third party one.

You can find the editor here and yes it’s inspired by the WDQS GUI.

How to setup a SPARQL endpoint with SOCH data?

First I usually bulk download RDF records with SOCH download CLI. Secondly I load these into Fuseki (super easy to setup, but somewhat low performance if you load all of SOCH into it) or Blazegraph (trickier to setup but great performance).

Observations on AI Assisted Crowdsourcing

12th November 2019

As a part of the “Wikimedia Commons Data Roundtripping” project facilitated by me in my role at the Swedish National Heritage Board we ran a pilot together the Swedish Performing Arts Agency around crowdsourcing translations of image descriptions this spring. In this post I’m briefly sharing some of my observations related to how AI assisted translations impacted the crowdsourcing campaign.

So to begin what did we do? We uploaded 1200 images to Wikimedia Commons all with descriptions in Swedish. Then we invited people to translate those descriptions into English using a tool built for that specific purpose.

screenshot of the user interface made for the crowdsourcing campaign

In addition to the empty input field for the user to fill in there was also the “Google Translate” button. This button would prefill the input with the automatic translation from Google (the user would still need to edit/submit it). Except it was a little easier said then done…

Every now and then there would be an encoding error in the string returned from Google:

“Anna-Lisa Lindroth as Ofelia in the play Hamlet, Knut Lindroth​\'​s companion 1906. Scanned glass negative”

This type of errors were discovered during development of the tool but we decided to leave the issue there to see if users would catch it.

A while into the crowdsourcing campaign it became clear that the descriptions translated by Google Translate were of higher quality then the ones done entirely by humans. While it didn’t manage all the theater specific terms it was still simply more consistent.

After an user had been using the Google Translate button for a while without many or any edits needed they no longer caught obvious error such as the example above. The pilot wasn’t large enough to prove anything statistically it indicates that users quickly starts trusting automated data if an initial subset of it is of high quality.

If you want to know more about the entire project that was much larger then this pilot there is plenty for you to read.

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