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Tag: Wikidata (Page 1 of 5)

Faster munging for the Wikidata Query Service using Hadoop

The Wikidata query service is a public SPARQL endpoint for querying all of the data contained within Wikidata. In a previous blog post I walked through how to set up a complete copy of this query service. One of the steps in this process is the munge step. This performs some pre-processing on the RDF dump that comes directly from Wikidata.

Back in 2019 this step took 20 hours and now takes somewhere between 1-2 days as Wikidata has continued to grow. The original munge step (munge.sh) makes use of only a single CPU. The WMF has been experimenting for some time with performing this step in their Hadoop cluster as part of their modern update mechanism (streaming updater). An additional patch has now also made this useful for the current default load process (using loadData.sh).

This post walks through using the new Hadoop based munge step with the latest Wikidata TTL dump on Google clouds Dataproc service. This cuts the munge time down from 1-2 days to just 2 hours using an 8 worker cluster. Even faster times can be expected with more workers, all the way down to ~20 minutes.

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How can I get data on all the dams in the world? Use Wikidata

During my first week at Newspeak house while explaining Wikidata and Wikibase to some folks on the terrace the topic of Dams came up while discussing an old project that someone had worked on. Back in the day collecting information about Dams would have been quite an effort, compiling a bunch of different data from different sources to try to get a complete worldwide view on the topic. Perhaps it is easier with Wikidata now?

Below is a very brief walkthrough of topic discovery and exploration using various Wikidata features and the SPARQL query service.

A typical known Dam

In order to get an idea of the data space for the topic within Wikidata I start with a Dam that I know about already, the Three Gorges Dam (Q12514). Using this example I can see how Dams are typically described.

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Creating new Wikidata items with OpenRefine and Quickstatements

Following on from my blog post using OpenRefine for the first time, I continued my journey to fill Wikidata with all of the Tors on Dartmoor.

This post assumes you already have some knowledge of Wikidata, Quickstatements, and have OpenRefine setup.

Note: If you are having problems with the reconciliation service it might be worth giving this mailing list post a read!

Getting some data

I searched around for a while looking at various lists of tors on Dartmoor. Slowly I compiled a list that seemed to be quite complete from a variety of sources into a Google Sheet. This list included some initial names and rough OS Map grid coordinates(P613).

In order to load the data into OpenRefine I exported the sheet as a CSV and dragged it into OpenRefine using the same process as detailed in my previous post.

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Using OpenRefine with Wikidata for the first time

I have long known about OpenRefine (previously Google Refine) which is a tool for working with data, manipulating and cleaning it. As of version 3.0 (May 2018), OpenRefine included a Wikidata extension, allowing for extra reconciliation and also editing of Wikidata directly (as far as I understand it). You can find some documentation on this topic on Wikidata itself.

This post serves as a summary of my initial experiences with OpenRefine, including some very basic reconciliation from a Wikidata Query Service SPARQL query, and making edits on Wikidata.

In order to follow along you should already know a little about what Wikidata is.

Starting OpenRefine

I tried out OpenRefine in two different setups both of which were easy to set up following the installation docs. The setups were on my actual machine and in a VM. For the VM I also had to use the -i option to make the service listen on a different IP. refine -i 172.23.111.140

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Wikidata Map May – November 2019

It’s time for another blog post in my Wikidata map series, this time comparing the item maps that were generated on the 13th May 2019 and 11th November 2019 (roughly 6 months). I’ll again be using Resemble.js to generate a difference image highlighting changed areas in pink, and breakdown the areas that have had the greatest change throughout the 6 month period. The full comparison image can be found here.

Differences in the Wikidata map highlights in pink for changes between May 2019 and November 2019

If you don’t know what Wikidata is, or what items are then give this page a read. This map shows all items that have a “coordinate location” as a light pixel on a black canvas. The more items with coordinates in a single pixel, the brighter that pixel. This map is generated using code that can be found here.

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Covid-19 Wikipedia pageviews, a first look

World events often have a dramatic impact on online services. A past example would be the death of Michael Jackson which brought down Twitter and Wikipedia and made Google believe that they were under attack according to the BBC.

Events like the COVID-19 (Coronavirus) pandemic have less instantaneous affect but trends can still be seen to change. Cloudflare recently posted about some of the internet wide traffic changes due to the pandemic and various government announcements, quarantines and lockdowns.

Currently the main English Wikipedia article for the COVID-19 pandemic is receiving roughly 1.2 million page views per day (14 per second). This article has already gone through 4 different names over the past months, and the pageview rate continues to climb.

Wikipedia pageviews tool showing English Wikipedia COVID-19 pandemic article views up to 21 March 2020 (source)
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Your own Wikidata Query Service, with no limits

The Wikidata Query Service allows anyone to use SPARQL to query the continuously evolving data contained within the Wikidata project, currently standing at nearly 65 millions data items (concepts) and over 7000 properties, which translates to roughly 8.4 billion triples.

Screenshot of the Wikidata Query Service home page including the example query which returns all Cats on Wikidata.

You can find a great write up introducing SPARQL, Wikidata, the query service and what it can do here. But this post will assume that you already know all of that.

Guide

Here we will focus on creating a copy of the query service using data from one of the regular TTL data dumps and the query service docker image provided by the wikibase-docker git repo supported by WMDE.

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Wikidata Map July 2019

It’s been another 9 months since my last blog post covering the Wikidata generated geo location maps that I have been tending to for a few years now. Writing this from a hammock, lets see what has noticeably changed in the last 9 months using a visual diff and my pretty reasonable eyes.

Wikidata “Huge” map generated on the 13th May 2019
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Wikidata Architecture Overview (diagrams)

Over the years diagrams have appeared in a variety of forms covering various areas of the architecture of Wikidata. Now, as the current tech lead for Wikidata it is my turn.

Wikidata has slowly become a more and more complex system, including multiple extensions, services and storage backends. Those of us that work with it on a day to day basis have a pretty good idea of the full system, but it can be challenging for others to get up to speed. Hence, diagrams!

All diagrams can currently be found on Wikimedia Commons using this search, and are released under CC-BY-SA 4.0. The layout of the diagrams with extra whitespace is intended to allow easy comparison of diagrams that feature the same elements.

High level overview

High level overview of the Wikidata architecture

This overview shows the Wikidata website, running Mediawiki with the Wikibase extension in the left blue box. Various other extensions are also run such as WikibaseLexeme, WikibaseQualityConstraints, and PropertySuggester.

Wikidata is accessed through a Varnish caching and load balancing layer provided by the WMF. Users, tools and any 3rd parties interact with Wikidata through this layer.

Off to the right are various other external services provided by the WMF. Hadoop, Hive, Ooozie and Spark make up part of the WMF analytics cluster for creating pageview datasets. Graphite and Grafana provide live monitoring. There are many other general WMF services that are not listed in the diagram.

Finally we have our semi persistent and persistent storages which are used directly by Mediawiki and Wikibase. These include Memcached and Redis for caching, SQL(mariadb) for primary meta data, Blazegraph for triples, Swift for files and ElasticSearch for search indexing.

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