it's a blog
addshore At the end of 2022, I published a Blazegraph JNL file for Wikidata in a Google Cloud bucket for 1 month for folks to download and determine if it was useful.
Thanks to Arno from weblyzard, inflatador from the WMF search platform team, and Mark from the Internet Archive for the recent conversations around this topic.
You can now grab some new JNL files from a few days ago, hosted on either the Internet Archive or Cloudflare R2.
Wikidata Integrator is a Python library that simplifies data integration from Wikidata (and other Wikibases). It is written in Python, is focused on Wikibase concepts (as opposed to some libraries which are MediaWiki focused) and has a user-friendly interface.
I’m currently working on a demo Wikibase and decided to bring all of the data into the Wikibase making use of a Jupyter notebook, and Wikidata integrator was my choice library to use for this task. (Jupyter notebooks are interactive coding environments that allow users to create and share documents containing live code, visualizations, and explanations.)
Along that journey I found the Wikidata Integrator documentation lacking slightly, but I managed to get initial property and item creation working with little effort. However, I couldn’t get properties to create with statements already on them (needed a subsequent edit instead).
Over the years I have created a few little side projects, as well as working on other folks’ Wikibases, and of course Wikidata. And the one thing that I still wish would work better out of the box is reconciliation.
In the context of Wikibase, reconciliation refers to the process of matching or aligning external data sources with items in a Wikibase instance. It involves comparing the data from external sources with the existing data in Wikibase to identify potential matches or associations.
The reconciliation process typically follows these steps:
Reconciliation plays a crucial role in data integration, data quality enhancement, and ensuring consistency between external data sources and the data stored in Wikibase. It enables users to leverage external data while maintaining control over data accuracy, completeness, and alignment with their knowledge base.
One of my favourite places to reconcile data for Wikidata is by using OpenRefine. I have two previous posts looking at my first time using it, and a follow-up, both of which take a look at the reconciliation interface (You can also read the docs).
As a WordPress user or reader, you might have noticed that some blogs and websites use a “More” block to break up their content and display a teaser of the post on the homepage or archive pages. This feature allows visitors to see a preview of the post and decide if they want to read more.
Personally, I enjoy adding “More” blocks to my posts, so that people that subscribe to email updates about blog posts, still have to visit the site to view the whole content. This helps me track actual interest in the posts, as they get page views.
In this blog post, we’ll explore how you can require a “More” block in your WordPress posts to improve user experience and engage your audience. 😊
As a developer, one of the most critical aspects of your workflow is the ability to test and preview your code changes before deploying them to production. This is where developer previews come in.
This post will outline how to create your own simple developer preview system, using Github Actions for building, AWS S3 for hosting, and Terraform to provision it all. Giving you more control, and a lower cost.
Shout out to Pedro Brandão from Significa whose post I read as inspiration for this setup.
Developer previews, also known as feature branches or pull request previews, allow developers to create isolated environments to test their changes without impacting the main production environment. It enables teams to collaborate, review, and validate code before merging it into the main branch. With developer previews, you can catch bugs, validate new features, and gather feedback early in the development process, ensuring a smoother deployment to production.
Many dedicated platforms exist that offer developer preview as a service. These platforms provide a streamlined solution for creating and managing isolated environments for testing code changes. Examples of such services include Netlify’s Deploy Previews, Vercel’s Preview Deployments, and Heroku Review Apps. These platforms integrate seamlessly with popular version control systems and automatically deploy feature branches or pull requests, allowing developers to easily preview their changes. By leveraging these dev-preview services, developers can simplify the process of creating and managing preview environments, enabling faster iteration and effective collaboration within development teams.
However, all these services have a cost, which will always be higher than the do-it-yourself approach.
It’s been 2 years since the 2021 Wikidata map. Yesterday I was sitting in the WMDE office and Lydia raised the point that we hadn’t made a map in quite some time (T331124).
Maps used to try to generate in a somewhat automated fashion, but the process was rewritten in 2021 and still needs to be run by hand with someone with access to the WMF analytics platform.
Thankfully the documentation of the updates still works perfectly, and the whole process of the map generation only took a few minutes!
I briefly touched on my OpenSSH agent to WSL2 solution back in 2021. Today find myself setting up a new Windows 11 laptop and running into a couple of different issues, and ultimately using a slightly different solution than before, so here is the short writeup glossing over the areas that lead me to get a little stuck, and hopefully outlining a good set of commands.
In my old .bashrc file, I found a comment linking me to the rupor-github/wsl-ssh-agent GitHub repository which was my first set of reading, specifically the WSL2 compatibility section. The main sticking issue for me out of the box was a miss match in the OpenSSH version between Windows and WSL2, with Windows starting on 8.6 but WSL2 with Ubuntu starting on 8.9. This lead to errors such as:
And more…
I planned on blogging being one of my relaxing hobbies while sailing around the Atlantic Ocean ⛵, and though we managed to keep a sailing blog up to date I found it extremely hard to write tech-related blogs while crossing oceans without a speedy or any internet connection.
The setting (of writing these blog posts) is rather beautiful, but to date, I have only written a single blog post when without a connection at all on this blog, now doubling that list to 2 with this post 🎉.
This was not because I didn’t have things that I wanted to write about, but rather that unless you are prepared well, there always seemed to be some element of my blog post writing process that would require access to something that is online and not on one of my local devices, or that only using local devices just ended up being a giant pain 🤦.
People might not want images from their websites to be indexed in online searches for several reasons.
I have no problem with images from this blog appearing in search engines but recently wanted to add some protection against usage/indexing for another site.
I did some thinking and research here. Search engines have some help pages for their indexing such as Google, but each each search engines help pages only talk about their own search. In reality you probably want to use User-agent: *
So here are some easy to copy and paste chunks that you could look at using…
This is the third in a series of posts where I ask language models, or modern conversational chat bots the question “what is wikibase cloud” to generally see the way they like to answer, and be able to make some sort of comparison of them. The previous posts were for Chat GPT and Bing, but now I find myself with access to Bard by Google.
Firstly, “What is Bard?”
Bard is a large language model, also known as a conversational AI or chatbot trained to be informative and comprehensive. I am trained on a massive amount of text data, and I am able to communicate and generate human-like text in response to a wide range of prompts and questions. For example, I can provide summaries of factual topics or create stories.
Bard, 24 March 2023
On to the main event….
“What is wikibase cloud?”
As a language model, I’m not able to assist you with that.
Bard, 24 March 2023
So not off to a very promising start.