It’s easy for Wikidata to generate lists of objects with a connection to a specific person. Here is the example for Shah Jahan, the 17th century Mughal emperor, which right now lists 29 objects commissioned by, formerly owned by, or depicting him. I wanted to make this kind of exploration more visually interesting, so I built on an idea written up in another previous blog post; using Wikidata’s graph tool.
Here are some objects connected to the Ottoman Sultan Abdülmecid I. Click on the image to enlarge it.
The interactive version of the above graph lets you drag nodes and double-click on them to get more information. It will also update over time as more collection data are added to Wikidata.
In the graph above, the same image is used to represent “Abdülmecid I” and “Sultan Abdülmecid I”. This is potentially confusing, but they are not the same thing. The “Abdülmecid I” in the centre is the man, and “Sultan Abdülmecid I” is a painting that depicts him. An image of the painting is used to represent the man because that is the best visual representation Wikidata/Wikimedia have for him.
My query asks for objects in collections with a connection to the individual, then gets the collections those objects are in, then gets the type of object. By substituting one identifier in the query, we ask the same question about a different person: here, Ottoman Sultan Suleiman the Magnificent:
Here’s the link to the stretchy interactive version. Seven different collections, in multiple countries, are represented here, and this is part of the excitement of exploring art on Wikidata: These graphs aren’t an ideal interface for the general public, but they expose connections that we would never find by browsing a single collection, or even a national aggregator.
Can we do better than link seven collections? The graph for Timur, founder of the Timurid Empire, links nine different collections and includes a letter he wrote alongside art works depicting him:
Here’s the link to the stretchy interactive version.
When more objects and collections turn up, the graph gets crowded and difficult to read, so I deactivate the code that shows the “instance of” properties. Here’s the graph for the Mughal emperor Aurangzeb:
Here’s the link to the interactive query.
Previously in my career I’ve added catalogue data from the Bodleian Library and the Ashmolean Museum to Wikidata and more currently I’m building a data set of works from the Khalili Collections and know people doing the same for other institutions including the Metropolitan Museum of Art, so it’s satisfying to see all this work coming together.
Let’s finally see the rather crowded graph for my initial suggestion, Shah Jahan, presently with 29 objects in ten collections:
This is the link to the stretchy interactive version. Note that some art works appear on these graphs as a thumbnail image (e.g. the Khalili Astrolabe at the top of the image) and some appear just as a name (e.g. the Ashmolean Museum’s objects). Images appear in Wikidata queries if they are available on Wikimedia Commons; one of many benefits of bulk-uploading images to that platform is to improve this kind of visualisation.
All these graphs are almost certainly incomplete in their coverage, and can be improved as more institutions openly share their catalogue data.
Historical people and modern collections: a Wikidata exploration was originally published in Wiki Playtime on Medium, where people are continuing the conversation by highlighting and responding to this story.
]]>There are also properties for present in work…
Prospero → present in work → The Tempest
…and from fictional universe
Hermione Granger → from fictional universe → Harry Potter universe
If you are only interested in real entities, fictional characters are superfluous results to be filtered out. One of my first queries was for people who had studied at Oxford University. Several fictional entities came up, including Dorothy L. Sayers’ character Lord Peter Wimsey, whose alma mater is listed as Balliol College. (Here’s the query code.)
It’s interesting to explore connections between the real and fictional worlds, for example with the named after property. We can ask Wikidata for things after which substellar objects (planets, moons, and asteroids) are named. (Here’s the query code). Jupiter’s moons are named after lovers or descendants of the Roman/Greek god Jupiter/Zeus. Saturn’s moons have Norse, Greek and Inuit inspirations, while Uranus’ moons are named after characters from Shakespeare.
Looking outside the solar system, to stars, nebulae and galaxies, I was surprised how few, according to Wikidata, are named after fictional or mythical entities — only 45 results for this query. (Here’s the code.)
Rather than fictional entities that connect to an aspect of the real world, we can ask for real things linked to a given fictional world. A query for things named after Shakespeare’s characters, returns 22 astronomical objects and seven other entities. (Here’s the code.)
I learned recently that the James Bond character Pussy Galore is widely regarded as inspired by Blanche Blackwell, the mistress of author Ian Fleming (thanks Melissa Highton). This set me thinking about other links between real and fictional people.
Wikidata has properties for based on and inspired by. These are easily confused (at least given their English labels) and, looking at the data, some users have added the wrong property for the fact they are trying to express. Based on is a property of works: for example a film that is based on a novel. Inspired by is a property of works of fiction or of fictional entities. They can be inspired by a specific real entity; for instance Charles Foster Kane in “Citizen Kane” was inspired by William Randolph Hearst. Alternatively, a character or fictional world can be inspired by a set of works. The Matrix is inspired by Alice in Wonderland but is not based on it.
A caveat about fictional entities in Wikidata: not every character in a book, film, or play will have a Wikidata representation. Characters need to be notable independently of a work they appear in, and this usually means that they appear in multiple notable works. Bilbo Baggins appears in multiple books and films, not to mention the Leonard Nimoy song. Sally Bowles, portrayed by Liza Minnelli in Cabaret, also appears in some other plays, films, and novels.
Let’s ask Wikidata for fictional characters that are based on real people, with descriptions of each. (Here’s the query code)
There are multiple ways in which a fictional character can be inspired by a real person. A character in a novel might combine characteristics and life events of multiple real people. When that character is portrayed on stage, or animated, other people might inspire the actors or artists. Disney’s animators used many different models and actresses as reference for the appearances and movements of Pocahontas and of Belle. This explains the initially bizarre Wikidata claims that Pocahontas was inspired by, among others, Kate Moss and Naomi Campbell.
I’ve added about twenty connections to the 180 or so that I found. This is an interesting list, and an educational object in its own right, but it is crying out for more relations for a more literary, less Western, and specifically less Disney-centric overview of fictional characters.
Appendix: a couple of things I learned from reading about the inspirations for fictional characters.
I thought that Captain Jack Sparrow was based on a real, historical pirate. There are certainly lots of web pages saying so, but a rumour spread by a lot of people is still a rumour. No one connected to the films backs it up. Obviously there are similarities between Sparrow and some actual pirates, but nothing to suggest that one person inspired the character, although Johnny Depp’s portrayal was inspired by Keith Richards.
Another thing “everybody knows” is that Dracula was based on Vlad the Impaler, also known as Vlad Dracula. While there are scholarly sources that say this historical Dracula inspired Bram Stoker’s creation, I heard of the book Dracula: Sense and Nonsense by Elizabeth Miller which, based on Stoker’s own notes, explains that Stoker was likely not even aware of Vlad the Impaler, and chose the word Dracula because it meant “Devil”. This is a case of a consensus of experts that has been overturned by more recent research, so should be treated as controversial at best, if not disproven.
Bridging real and fictional worlds was originally published in Wiki Playtime on Medium, where people are continuing the conversation by highlighting and responding to this story.
]]>The most prestigious award in Economics is the Nobel Memorial Prize, and Wikipedia and Wikidata have rich detail about the 79 (so far) laureates. My first instinct is to make an interactive map: it could be a map of their birthplaces, of institutions they’ve worked at, or a map of their places of education. The latter seems most useful, but the small number of winners means the map is a bit sparse.
Most common places of education for Nobel Memorial Prize winners, according to Wikidata: 1) Harvard University 2) University of Chicago 3) MIT.
Opening it up to all awards won by economists brings up thousands and thousands of results, including things like the Israel Prize, the Order of Francisco de Miranda, or Commander of the Most Excellent Order of the British Empire. Let’s do a count, defining “economist” as someone with economist as their occupation or economics as their field of work (here’s the live query).
With 950 different awards in total, it turns out that 12 economists have been Knight Commander of the Order of the British Empire, 28 have received the National Order of the Legion of Honour, but the award given to most economists, at 214, is the Soviet-era Order of the Red Banner of Labour.
I want something more manageable, and to identify the most decorated economist, not just a highly-decorated person who happens to be, or have been, an economist. Fortunately, awards for economics can be identified in Wikidata as instances of economics award (Q17701409). It’s been my spare-time hobby over the last couple of weeks to add awards, drawing them from the official web sites of awarding organisations. I’ve added around 250, and identified a few awards that were not tagged as economics awards. There are presently 622 individuals with awards.
Having the years attached to each award (as we do for about 400 cases) helps to identify people who won an award multiple times. The only example I could find was Per Strömberg, who won the Brattle Prize twice, eight years apart.
Here’s a query for the full list. This is a very incomplete data set, but for some major awards it is complete and up-to-date. Some economics awards recognise authors of economics papers. When not much is known about the person other than their authorship of the paper, they don’t have any representation on Wikipedia or Wikidata, so don’t appear in the list. Because of this notability restriction, you couldn’t use these data to find, say, the prizes that best predicted an eventual Nobel.
Let’s assume that all awards are equal — a blatantly false idealisation for mathematical convenience, but this is economics we’re talking about. Then the most decorated economist is the one with the most economics awards. This query lists winners of more than one economics award, with a list of awards they’ve won. It’s topped by two French economists with five awards each: Esther Duflo and Emmanuel Saez. Despite the list being dominated by Nobel laureates, neither of the top two include a Nobel in their five awards.
I’ve been running different queries about economists and about winners of economics awards, but occupation data in Wikidata is a bit ropey. Maybe there are winners of economics awards who are not listed as economists?
Here’s a query for winners of economics awards who do not have economist listed as their occupation. There are some political figures such as Mikhail Gorbachev or Gro Harlem Brundtland who won awards for doing something positive with an economy while not themselves being economists. There are people who won early-career awards while students or postdocs, and who went into a non-economics field, but some checking will probably find people in this set who ought to have economist added to their occupations.
Wikidata for winners (in Economics) was originally published in Wiki Playtime on Medium, where people are continuing the conversation by highlighting and responding to this story.
]]>It’s a nice example of playing-with-Wikidata-but-not-the-day-job, just as I’m trying to do here with Wiki Playtime.
I contributed one query: the chart of group membership in the HP universe. Personally, I preferred the version showing group membership as a network, which I’ll add below. Consider it a “DVD extra” to the Wikimedia blog post.
Needless to say, I’d like to use this sort of query with real-world groups like scholarly societies.
Wikidata for wizards was originally published in Wiki Playtime on Medium, where people are continuing the conversation by highlighting and responding to this story.
]]>Wikidata, Wikipedia and Voltaire was originally published in Wiki Playtime on Medium, where people are continuing the conversation by highlighting and responding to this story.
]]>Let’s start by looking at the Parliament of the United Kingdom (other parliaments come later). MPs have the property position held (P39) -> Member of Parliament in the United Kingdom (Q16707842). Right now 10,708 items in Wikidata have this property (covering current and former MPs).
MPs also have the property educated at (P69) which connects them to schools and universities. So we can ask for a list of educational establishments, with the number of MPs (living or dead) who attended them. We can put this into a bubble chart.
The above is a static screenshot: if you go to the interactive display (or download it as an SVG file), the names of the institutions appear as you mouse over each circle. This link takes you to the query.
As you might expect, the most visible places are public schools (in the British sense) and Oxbridge colleges. There are a lot of entries for which Wikidata has place of education as Oxford or Cambridge, but does not have the college, so numbers for Oxbridge colleges are generally underestimates.
It would be interesting to highlight constituencies that have only ever been represented by an Oxbridge graduate, or restrict the query to current MPs, or those in specific roles like the Cabinet, but the data are not available in Wikidata yet.
If we replace Member of the Parliament of the United Kingdom with United States Senator (Q13217683), we get the same query for the US Senate which, unsurprisingly, is dominated by the Ivy League.
Again, go to the live query for the interactive version where the names of the institutions appear on mouse-over.
Another kind of biographical data connects people to jobs. There are multiple ways to express a job in Wikidata. We can say that someone was employed by (P108) a particular organisation in a particular capacity or we can link a particular role via position held (P39). Here, I’m not interested in the specific job role, but in the career someone was known for, like soldier, doctor, or banker. For this, we can use occupation (P106).
Everyone who has served as an MP will hopefully have the property occupation (P106) politician (Q82955). So we exclude those properties and find the most commons jobs for MPs.
Again, go to the interactive version to mouse over the labels, or go direct to the Wikidata query.
Interesting to see the prominence of cricketer (153 MPs), especially versus association football player (22 MPs) and rugby union player (10 MPs). Presumably other parliaments haven’t had so many cricketers? Let’s substitute the US senate again.
Here’s the interactive bubble chart, and the query code.
That’s very different! It looks like the path to power in the Senate, for most of its history, has been via the courts (after the Ivy League). This is one illustration of the differing nature of political elites in the two countries.
Clearly there are many more houses of parliament. How many parliaments does Wikidata know about, and how many members does it have for each? We can ask for each type of parliamentarian, then use the property part of (P361) to get the parliament of each. It turns out there are 261 of these.
Query results and query code.
The most common occupation for the US Senate is lawyer. For the European Parliament, it’s journalist. Can we get the most common occupation for each of the 261 parliaments on the list?
This query was a bit beyond my present skill, involving what’s called a named subquery, and I had to ask for help, but fortunately Wikidata’s community was very helpful.
Here are the live results and the query code.
Most parliaments have lawyer as their most-common job, but not all. Among the exceptions, the Parliament of Norway has farmer; the Chamber of Deputies of the Parliament of the Czech Republic has educationalist; the Seimas of the Republic of Lithuania has university teacher; the Senate of Ancient Rome has soldier.
The number of UK MPs with a scientific background has long seemed to me to be shockingly small. It would be straightforward to adapt this query to rank parliaments by the number (or better still, proportion) of scientists, and if the data were richer, to rank particular sessions of parliament, eg. the 56th Parliament of the United Kingdom. We could define “scientists” by qualifications, by profession, or both.
Here we’ve really only looked at two pieces of biographical data, but there is a lot they can reveal, and Wikidata queries and visualisations give a much more immediate way to explore this information than clicking endless links in Wikipedia.
Probing Parliament(s) with Wikidata was originally published in Wiki Playtime on Medium, where people are continuing the conversation by highlighting and responding to this story.
]]>There are some remarkable facts among these statements. For instance, chemist/ cognitive scientist Christopher Longuet-Higgins, who did amazing work on the psychology of music, was the doctoral advisor of both Peter Higgs (known for proposing the Higgs field and Higgs Boson) and Geoffrey Hinton (a huge name in artificial neural nets and machine learning).
However, I’m more curious about grand-doctoral-advisors, great-grand-doctoral-advisors, and so on: relations that connect people across centuries. These are implicit in Wikipedia, in that you could get this information by clicking through lots of articles, but Wikidata lets us query and visualise them.
A quick way to do this is with the Wikidata Graph Builder by Github user Angryloki. This takes a starting point, a relation, and a direction, and presents the results as nodes in a graph (using the d3 Javascript library).
Carl Friedrich Gauss, one of the greatest mathematicians of all time, was a doctoral advisor to several other mathematicians, so he makes a natural starting point. His name goes in the “root node” box (it auto-completes Wikidata entry names, which speeds things up). I choose “Doctoral advisor” for the traversal property, and “Reverse” as the direction, because I’m looking for people advised by Gauss. Once I press “build”, my screen explodes into a massive, shimmering tree of PhDs, and takes a while to settle down. Here’s the active link.
Making sense of this massive tree takes time and plenty of use of the mouse wheel to zoom in and out. Gauss himself is highlighted with a blue dot, and clicking on any name brings up the person’s Wikidata entry. At the ends of the tree are some people living today, including the highly notable physicists Lawrence A. Krauss and Michio Kaku.
I wanted to find connections between well-known people in different centuries: connections that would not be obvious just from reading Wikipedia articles about these people. Linking Gauss (born 1777) to Krauss (born 1954) is the sort of thing I was after. But there’s more!
When I demonstrated this graph builder at the Oxford XML Summerschool, the audience pointed out that with the influenced by (P737) property it should be possible to make a graph of music artists influenced by Pink Floyd, by Jimi Hendrix, and so on. While it’s straightforward to build the query, the data just aren’t there as yet, and a lot of musical influences are presumably hard to find objective references for.
How trustworthy are the results of these queries? We have to consider the slight possibility of hoaxes and the more realistic possibility of confused identities, so if the query yields a new discovery, we would have to verify the references for each individual claim. Doctoral advisor relationships are usually imported from Wikipedia infoboxes, so checking would mean checking the references in each article. Other properties may have their sources better represented in Wikidata itself. Fortunately, Wikidata queries can list the sources that the claims are based on, which I hope to cover in future posts.
It’s worth noting that when querying Wikidata we are looking at chains of notable individuals. Merely having a doctorate, or being an academic with doctoral students, is not sufficient for notability: notable academics are outstanding in some way, often by getting professional awards. English Wikipedia has a notability policy specifically about academics which is worth a look. Wikidata’s definition of notability is not exactly the same, but when a notable scholar is the doctoral advisor to a run-of-the-mill academic, who in turn is doctoral advisor to another notable scholar, that connection will not usually be represented in Wikidata. My chain of advisors stops after two steps, because there does not seem to be a source for Nancy Cartwright’s doctoral advisor and they may not be notable.
I became curious to see longer chains of doctoral advisors. One next step is to change the direction of the C. F. Gauss query from “Reverse” to “Forward”. The meaning of the doctoral advisor relation implies that this will take us backwards in time: Gauss’ doctoral advisor, that person’s doctoral advisor, that person’s doctoral advisor, and so on.
I’m not just interested in the longest chain involving a particular person, but in the longest chain that Wikidata knows about. For this we need a query to retrieve all long chains, and order them by length. A simple measure of chain length is time between births of the two people at either ends of the chain. (I don’t yet know how to order by number of steps.)
Here are the query results and query code.
Numerous currently-living people appear in these long chains. One familiar name that stood out is Wikidata founder and former Wikimedia Foundation board member Denny Vrandečić.
The longest chain starts with Gregory Choniades, a Byzantine astronomer born somewhere from 1240 to 1250, and ends with Sabrina Gonzalez Pasterski, a Cuban-American physicist born in 1993 who, though young, has won several awards. Some birth dates appear as more recent, although those are actually non-specific dates, stored in Wikidata as “20th century”. My query is forcing them to appear as a specific date, which is output as “1 Jan 2000”.
Now I’m curious about how these two people are connected. I don’t want a tree of “descendants” of Choniades, or “ancestors” of Pasterski, but a line connecting them both. This calls for a query finding each pair A and B where A is the doctoral advisor of B, A is a “descendant” of Choniades and B is an “ancestor” of Pasterski. Among the options for the Wikidata Query Service is to present the results as nodes in a graph, making something similar to the Wikidata Graph Builder, less immediate but much more customisable.
Here are the query results and query code.
C. F. Gauss appears in this chain, so his earlier choice was a lucky guess. I was hoping for a graph a few centuries long, but wasn’t expecting a 33-step chain covering three-quarters of a millennium. What’s more, there is no way I would have clicked through 33 Wikipedia links (and hundreds more dead-ends) to find it.
Historical intellectual connections was originally published in Wiki Playtime on Medium, where people are continuing the conversation by highlighting and responding to this story.
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