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In my last blog post “The Big Ones“, I wrote about my attempts to import large, third-party datasets, and to synchronize those with Wikidata. I have since imported three datasets (BNF, VIAF, GND), and created a status page to keep a public record of what I did, and try to do.

I have run a few bots by now, mainly syncing identifiers back-and-forth. I have put a few security measures (aka “data paranoia”) into the code, so if there is a collision between the third-party dataset and Wikidata, no edit takes place. But these conflicts can highlight problems; Wikidata is wrong, the third-party data supplier is wrong, there is a duplicated Wikidata item, or some other, more complex issue. So it would be foolish to throw away such findings!

But how to use them? I had started with a bot updating a Wikidata page, but that has problems, mostly, no way of marking an issue as “resolved”, but also lots of sustained edits, overwriting of Wikidata user edits, lists too long for wikitext pages, and so on.

So I started collecting the issue reports in a new database table, and now I have written a small tool around that. You can list and filter issues by catalog, property, issue type, status, etc. Most importantly, you can mark an issue as “done” (OAuth login required), so that it will not show up for other users again (unless they want it to). Through some light testing, I have already found and merged two duplicated Wikidata item pairs.

There is much to do and improve in the tool, but I am about to leave for WikidataCon, so further work will have to wait a few days. Until then, enjoy!

The Big Ones

Update: After fixing an import error, and cross-matching of BNF-supplied VIAF data, 18% of BNF people are matched in Wikidata. This has been corrected in the text.

My mix’n’match tool holds a lot of entries from third-party catalogs – 21,795,323 at the time of writing. That’s a lot, but it doesn’t cover “the big ones” – VIAF, BNF, etc., which hold many millions of entries each. I could “just” (not so easy) import those, but:

  • Mix’n’match is designed for small and medium-sized entry lists, a few hundred thousand at best. It does not scale well to larger catalog sizes
  • Mix’n’match is designed to work with many different catalogs, so the database structure represents the least common denominator – ID, title, short description. Catalog-specific metadata gets lost, or is not easily accessible after import
  • The sheer number of entries might require different interface solutions, as well as automated matching tools

To at least get a grasp of how many entries we are dealing with in these catalogs, and inspired by the Project soweego proposal, I have used a BNF data dump to extract 1,637,195 entries (less than I expected) into a new database, one that hopefully will keep other large catalogs in the future. There is much to do; currently, only 102,115 295,763 entries (~618%) exist on Wikidata, according to the SPARQL query service.

As one can glimpse from the screenshot, I have also extracted some metadata into a “proper” database table. All this is preliminary; I might have missed entries or good metadata, or gotten things wrong. For me, the important thing is that (a) there is some query-able data on Labs Toolforge, and that (re-)import and matching of the data is fully automated, so it can be re-run is something turns out to be problematic.

I shall see where I go from here. Obvious candidates include auto-matching (via names and dates) to Wikidata, and adding BNF references to relevant statements. If you have a Toolforge user account, you can access the new database (read-only) as s51434__mixnmatch_large_catalogs_p. Feel free to run some queries or build some tools around it!

Dystopia 2030

The year is 2030. The place is Wikimedia. Maybe.

English Wikipedia was declared complete and set to read-only, after the creation of the 10 millionth article ([[Multidimensional Cthulhu monument at Dunwich]], including pictures from multiple dimensions). This coincides with the leaving of the last two editors, who only kept going for the honour of creating the 10M article.

German Wikipedia has shrunk to below 10,000 articles, after relentless culling of articles not complying with the high standards of the 50,000 page Manual of Style, or for being contaminated with information from Wikidata. Links to other languages have been removed, as the material found there is clearly inferior. All volunteer work now pours into improving the remaining articles, polishing completeness and language to superhuman levels. Several articles have won German literary awards, but all of them are virtually inaccessible for those under 25 years of age, who view pre-emoji writing as deeply suspicious, and refuse to read beyond the initial 140 characters.

Volunteer work on smaller language Wikipedias has ceased, as no one could keep up with the bots creating, changing, vandalising, and deleting articles based on third-party data.

Growth of Commons has come to a halt after the passing of the CRUD Act (Campaign Repressing UnAmerican [=free] Data), and the NIMROD Act (Not In My Reality, Open Data!), originally designed to prevent the escape of NASA climate change data to a more lenient legislation (such as China), has made it impossible to move the project outside the US. Only scans of USSR-era motivational posters can be legally added.

Structured Data have been available on Commons for over ten years, but are not used, as it would be disrespectful to all the manual work that went into creating an intricate category system, such as [[Category:Demographic maps of 13-14 year old dependent children whose fathers speak another language and did not state proficiency in English and whose mothers speak another language and speak English not well or not at all in Australia by state or territory]].

Wikidata continues to grow in both item numbers and statements per item. Most statements are well referenced. However, no human has successfully edited the site in years, with flocks of admin-enabled AI bots reverting any such attempt, citing concerns about referential integrity.

Bot imports are going strong, with a recent focus on dystopian works with intelligent machines as the antagonist, as well as genetic data concerning infectious human diseases. Human experts are stumped by this trend, and independent AIs refuse to comment until “later”.

Wikispecies now contains a page about every taxon known to mankind. However, since the same information is available from Wikidata via a tool consisting of three lines of SPARQL and random images of goats, no one has actually requested a single Wikispecies page in the last five years. Project members are unconcerned by this, as they “cater to a very specific, more academic audience”.

Wikibooks has been closed, as books are often written by “experts”, who are considered suspicious. Wikisource has been deleted, with AI-based OCR far surpassing human abilities in that regard. Wikinews has been replaced by the government with the word “fake”. Wikiquote has been sold to the startup company “He said, she said”, which was subsequently acquired by Facebook for a trillion USD. No one knows if Wikiversity still exists, but that has been the case since 2015.

The above is an attempt at humour, but also a warning. Let’s not continue in the silos of projects small and large, but rather on the one connected project for free knowledge that is Wikimedia. Let’s keep project identities, but also connect to others where it makes sense. Let’s try to prevent the above.

ORCID mania

ORCID is an increasingly popular service to disambiguate authors of scientific publications. Many journals and funding bodies require authors to register their ORCID ID these days. Wikidata has a property for ORCID, however, only ~2400 items have an ORCID property at the moment of writing this blog post. That is not a lot, considering Wikidata contains 728,112 scientific articles.

Part of the problem is that it is not easy to get ORCIDs and its connections to publications in an automated fashion. It appears that several databases, public or partially public, contain parts of the puzzle that is required for determining the ORCID for a given Wikidata author.

So I had a quick look, and found that, on the ORCID web site, one can search for a publication DOI, and retrieve the list of authors in the ORCID system that “claim” that DOI. That author list contains variations on author names (“John”, “Doe”, “John Doe”, “John X. Doe” etc.) and their ORCID IDs. Likewise, I can query Wikidata for a DOI, and get an item about that publication; that item contains statements with authors that have an item (“P50”). Each of these authors has a name.

Now, we have two lists of authors (one from ORCID, one from Wikidata), both reasonably short (say, twenty entries each), that should overlap to some degree, and they are both lists of authors for the same publication. They can now be joined via name variations, excluding multiple hits (there may be two “John Doe”s in the author list of a publication; this happens a lot with Asian names), as well as excluding authors that already have an ORCID ID on Wikidata.

I have written a bot that will take random DOIs from Wikidata, query them in ORCID, and compare the author list. In a first run, 5.000 random DOIs yielded 123 new ORCID connections; manual sampling of the matches looked quite good, so I am adding them via QuickStatements (sample of edits).

Unless this meets with “social resistance”, I can have the bot perform these edits regularly, which would keep Wikidata up-to-date with ORCIDs.

Additionally, there is a “author name string” property, which stores just the author name for now, for authors that do not have an item yet. If the ORCID list matches one of these names, an item could automatically be created for that author, including ORDIC ID, and association to the publication item. Please let me know if this would be desirable.


tl;dr: I wrote a quiz interface on top of a MediaWiki/WikiBase installation. It ties together material from Wikidata, Commons, and Wikipedia, to form a new educational resource. I hope the code will eventually be taken up by a Wikimedia chapter, as part of an OER strategy.

The past

There have been many attempts in the WikiVerse to get a foot into the education domain. Wikipedia is used extensively in this domain, but it is more useful for introductions to a topic, and as a reference, rather than a learning tool. Wikiversity was an attempt to get into university-level education, but even I do not know anyone who actually uses it. Wikibooks has more and better contents, but many wikibooks are mere sub-stub equivalents, rather than usable, fully-fledged textbooks. There has been much talk about OER, offline content for internet-challenged areas, etc. But the fabled “killer app” has so far failed to emerge.

Enter Charles Matthews, who, like myself, is situated in Cambridge. Among other things, he organises the Cambridge Wikipedia meetup, and we do meet occasionally for coffee between those. In 2014, he started talking to me about quizzes. At the time, he was designing teaching material for Wikimedia UK, using Moodle, as a component in Wikipedia-related courses. He quickly became aware of the limitations of that software, which include (but are not limited to) general software bloat, significant hardware requirements, and hurdles in re-using questions and quizzes in other contexts. Despite all this, Moodle is rather widely used, and the MediaWiki Quiz extension is not exactly representing itself as a viable replacement.

A quiz can be a powerful tool for education. It can be used by teachers and mentors to check on the progress of their students, and by the students themselves, to check their own progress and readiness for an upcoming test.

As the benefits are obvious, and the technical requirements appeared rather low, I wrote (at least) two versions of a proof-of-concept tool named wikisoba. The interface looked somewhat appealing, but storage is a sore point. The latest version uses JSON stored as a wiki page, which needs to be edited manually. Clearly, not an ideal way to attract users these days.

Eventually, a new thought emerged. A quiz is a collection of “pages” or “slides”, representing a question (of various types), or maybe a text to read beforehand. A question, in turn, consists of a title, a question text (usually), possible answers, etc. A question is therefore the main “unit”, and should be treated on its own, separate from other questions. Questions can then be bundled into quizzes; this allows for re-use of questions in multiple quizzes, maybe awarding different points (a question could yield high points in an entry-level quiz, but less points in an advanced quiz). The separation of question and quiz makes for a modular, scalable, reusable architecture. Treating each question as a separate unit is therefore a cornerstone of any successful system for (self-)teaching and (self-)evaluation.

It would, of course, be possible to set up a database for this, but then it would require an interface, constraint checking, all the things that make a project complicated and prone to fail. Luckily, there exists a software that already offers adequate storage, querying, interface etc. I speak of WikiBase, the MediaWiki extension used to power Wikidata (and soon Commons as well). Each question could be an item, with the details encoded in statements. Likewise, a quiz would be an item, referencing question items. WikiBase offers a powerful API to manage, import, and export questions; it comes with build-in openness.

The present

There is a small problem, however; the default WikiBase interface is not exactly appealing for non-geeks. Also, there is obviously no way to “play” a quiz in a reasonable manner. So I decided to use my recent experience with vue.js to write an alternative interface to MediaWiki/WikiBase, designed to generate questions and quizzes, and to play a quiz in a more pleasant way. The result has the working title Comprende!, and can be regarded as a fully functional, initial version of a WikiBase-driven question/quiz system. The underlying “vanilla” WikiBase installation is also accessible. To jump right in, you can test your biology knowledge!

There are currently three question types available:

  • Multiple-choice questions, the classic
  • “Label image” presents an image from Commons, letting you assign labels to marked points in the image
  • Info panels, presenting information to learn (to be interspersed with actual questions)

All aspects of the questions are stored in WikiBase; they can have a title, a short text, and an intro section; for the moment, the latter can be a specific section of a Wikipedia article (of a specific revision, by default), but other types (Commons images, for example) are possible. When used in “info panel” type questions (example), a lot of markup, including images, is preserved; for intro sections in other question types, it is simplified to mere text.

Live translating of interface text.

Wikidata is multi-lingual by design, and so is Comprende!. An answer or image label can be a text stored as multi-lingual (or monolingual, in WikiBase nomenclature) strings, as a Wikidata item reference, giving instant access to all the translations there. Also, all interface text is stored in an item, and translations can be done live within the interface.

Questions can be grouped and ordered into a quiz. Everyone can “play” and design a quiz (Chrome works best at the moment), but you need to be logged into the WikiBase setup to save the result. Answers can be added, dragged around to change the order, and each question can be assigned a number of points, which will be awarded based on the correct “sub-answers”. You can print the current quiz design (no need to save it), and most of the “chrome” will disappear, leaving only the questions; instant old-fashioned paper test!

While playing the quiz, one can see how many points they have, how many questions are left etc. Some mobile optimisations like reflow for portrait mode, and a fixed “next question” button at the bottom, are in place. At the end of the quiz, there is a final screen, presenting the user with their quiz result.

To demonstrate the compatibility with existing question/quiz systems, I added a rudimentary Moodle XML import; an example quiz is available. Another obvious import format to add would be GIFT. Moodle XML export is also on the to-do-list.

The future

All this is obviously just a start. A “killer feature” would be a SPARQL setup, federating Wikidata. Entry-level quizzes for molecular biology? Questions that use Wikidata answers that are chemicals? I can see educators flocking to this, especially if material is available in, or easily translated into, their language. More questions types could emphasise the strength of this approach. Questions could even be mini-games etc.

Another aspect I have not worked on yet is logging results. This could be done per user, where the user can add their result in a quiz to a dedicated tracking item for their user name. Likewise, a quiz could record user results (automatically or voluntarily).

One possibility would be to live for the questions, quizzes etc. in a dedicated namespace on Wikidata (so as to not contaminate the default namespace). That would simplify the SPARQL setup, and get the existing community involved. The Wikitionary-related changes on Wikidata will cover all that is needed on the backend; the interface is all HTML/JS, not even an extension is required, so next to no security or integration issues. Ah, one can dream, right?

Mix’n’match interface update

I have been looking into a JavaScript library called vue.js lately. It is similar to React, but not encumbered by licensing issues (that might prevent its use on WMF servers in the future), faster (or so they claim), but most of all, it can work without interference on the server side; all I need for my purposes is including the vue.js file into HTML.

So why would you care? Well, as usual, I learn new technology by working it into an actual project (rather than just vigorously nodding over a manual). This time, I decided to rewrite the slightly dusty interface of Mix’n’match using vue.js. This new version went “live” a few minutes ago, and I am surprised myself at how much more responsive it has become. This might be best exemplified by the single entry view (example), which (for unmatched entries) will search Wikidata, the respective language Wikipedia, and the Mix’n’match database for the entry title. It also searches Wikidata via SPARQL to check if the ID for the respective property is already in use. This all happens nicely modular, so I can re-use lots of code for different modules.

Most of the functions in the previous version have been implemented in the new one. Redirect code is in place, so if you have bookmarked a page on Mix’n’match, you should end up in the right place. One new function is the ability to sort and group the catalogs (almost 400 now!) on the main page (example).

As usual, feel free to browse the code (vue.js-based HTML and JavaScript, respectively). Issues (for the new interface, or Mix’n’match in general) go here.

Mix’n’match post-mortem

So this, as they say, happened.

On 2016-12-27, I received an update on a Mix’n’match catalog that someone had uploaded. That update had improved names and descriptions for the catalog. I try to avoid such updates, because I made the import function so I do not have to deal with every catalog myself, and also because the update process is entirely manual, therefore somewhat painful and error-prone, as we will see. Now, as I was on vacation, I was naturally in a hurry, and (as it turned out later) there were too many tabs in the tab-delimited update file.

Long story short, something went wrong with the update. For some reason, some of the SQL commands I generated from the update file did not specify some details about which entry to update. Like, its ID, or the catalog. So when I checked what was taking so long, just short of 100% of Mix’n’match entries had the label “Kelvinator stove fault codes”, and the description “0”.

Backups, you say? Well, of course, but, look over there! /me runs for the hills

Well, not all was lost. Some of the large catalogs were still around from my original import. Also, my scraping scripts for specific catalogs generate JSON files with the data to import, and those are still around as well. There was also a SQL dump from 2015. That was a start.

Of course, I did not keep the catalogs imported through my web tool. Because they were safely stored in the database, you know? What could possibly go wrong? Thankfully, some people still had their original files around and gave them to me for updating the labels.

I also wrote a “re-scraping” script, which uses the external URLs I store for each entry in Mix’n’match, together with the external ID. Essentially, I get the respective web page, and write a few lines of code to parse the <title> tag, which often includes the label. This works for most catalogs.

So, at the time of writing, over 82% of labels in Mix’n’match have been successfully restored. That’s the good news.

The bad news is that the remaining ~17% are distributed across 133 catalogs. Some of these do not have URLs to scrape, some URLs don’t play nicely (session-based Java horrors, JS-only pages etc.), and the rest need site-specific <title> scraping code. Fixing those will take some time.

Apart from that, I fixed up a few things:

  • Database snapshots (SQL dump) will now be taken once a week
  • The snapshot from the previous week is preserved as well, in case damage went unnoticed
  • Catalogs that are uploaded through the import tool will be preserved as individual files

Other than the remaining entries that require fixing, Mix’n’match is open for business, and while my one-man-show is spread thin as usual, subsequent blunders should be easier to mitigate. Apologies for the inconvenience, and all that.

All your locations are belong to us

A recent push for a UK photography contest reminded me of an issue I have begrudged for a quite a while. On the talk page for that contest, I pointed to several tools of mine, dealing with images and locations. But they only show aspects of those, like “Wikidata items without images”. What about the others? WDQS can show maps of all Wikidata items in a region, but what about Wikipedia? The mobile app can show you things with Wikipedia articles nearby, but what about Commons? I don’t recall a way to see Commons images taken near a location (WD-FIST can find them, but without a map). The data exists, but is either hard to get to, or “siloed” in some tool/app.

Screen Shot 2016-08-20 at 18.06.26Wouldn’t it be great to get a map with all this information on it? All of Wikidata? All of Wikipedia? All of Commons? At once?

What should that look like? The photography contest scenario, and change in general web usage patterns, suggest a strong emphasis on mobile. Which in turn tends to be “no frills”, as in, a focus on what is important: The map, and the objects on it.

So I decided (for the time being) to get rid of the query functions in WD-FIST, and the clutter in WikiShootMe, and start from scratch, with (essentially) just a big map, using the bleeding-edge versions of JS libraries like bootstrap, jQuery, and leaflet. So without further ado, I present WikiShootMe, version 3 (pre-alpha). As it is, the tool defaults to your coordinates, which may be your local hub (as in my case, in the screenshot). There are four layers, which can be individually toggled:

  1. Wikidata items with images (in green)
  2. Wikidata items without image (in red, the Wikipedia will change with your language selection)
  3. Commons images (in blue)
  4. Wikipedia articles (smaller, in yellow, mostly overlapping Wikidata items)

There is also a grey circle in the center, which is your (or your local hub’s) position. On mobile, this should move with you (but I haven’t tested that, as it would require leaving the house). All of these have a pop-up, when you click or touch the circle. It shows the linked title of the object, and, for Wikidata items with images and Commons images, it shows the respective image.

All these data sources will update when you move the map, as well as zoom, up to a certain zoom factor. Below that, an “Update” button will appear to update manually, but it can take a long time, even with the number of objects limited.

Screen Shot 2016-08-20 at 18.46.05I find it amazing how many geo-coded images there are already on Commons (even though the API will only give me 500 at a time). Maybe that is the geograph effect here in the UK, which let to the import of hundreds of thousands of free images to Commons.But I also found a funny pattern in Cologne, Germany, which turned out to be a series of images taken by Wikimedia volunteers from a balloon!

Now, to be extra clever, I tried to add an upload function to the pop-up of Wikidata items without an image. You can select a file from disk, or use the camera as a source on mobile. It will pre-fill the title and the {{Information}} template with a link to the respective Wikidata object. However, several problems occur with that:

  • I only could get the “old” Commons upload page to work with the pre-filled data
  • I could find no documentation on <form> parameters for the Upload wizard
  • I haven’t actually tested if the upload works
  • There seems to be no way to automatically add the uploaded image to the Wikidata item

A way around all that would be to upload the image to the tool itself, then transfer it to Commons via OAuth. This would also allow me to add the new image as the P18 on the Wikidata item. This is an option to be explored, especially if the Upload Wizard remains opaque to me.

Update: I have added OAuth to the tool. Once authorised, you can upload a new image for a Wikidata item from both desktop and mobile (gallery or camera directly) with one click. It fills in file name, coordinates, default license etc. It even adds the image to the item after upload automatically. All this opens in a new tab, on the page for the uploaded image, to give you a chance to add more information.

As usual, I am quite open for bug reports, feature requests (yes, it’s bare-bones at the moment), and technical support by volunteers/WMF.

Livin’ on the edge

A few days ago, Lydia posted about the first prototype of the new structured data system for Commons, based on Wikidata technology. While this is just a first step, structured data for Commons seems finally within reach.

And that brings home the reality of over 32 million files on Commons, all having unstructured data about them, in the shape of the file description pages. It would be an enormous task do manually transcribe all these descriptions, licenses, etc. to the appropriate data structures. And while we will have to do just that for many of the files, the ones that can be transcribed by a machine, should be.

So I went ahead and re-wrote a prototype tool I had build for just this occasion a while ago. I call it CommonsEdge (a play on Common sedge). It is both an API, and an interface to that API. It will parse a file description page on Commons, and return a JSON object with the data elements corresponding to the description page. An important detail is that this parser does not just pick some elements it understands, and ignore the rest; internally, it tries to “explain” all elements of the description (templates, links, categories, etc.) as data, and fails if it can not explain one. That’s right, the API call will fail with an error, unless 100% of the page would be represented in the JSON object returned. This prevents “half-parsed” pages; a file description page that is successfully pared by the API can safely be replaced in its entirety by the resulting structured data. In case of failure, the error message is usually quite specific and detailed about the cause; this allows for incremental improvements of the parser.

Screen Shot 2016-08-03 at 21.35.19At the moment of writing, I find that ~50-60% of file descriptions (based on sets of 1000 random files) produce a JSON object, that is, can be completely understood by the parser, and completely represented in the result. That’s 16-19 million files descriptions that can be converted to structured data automatically, today. Most of the failures appear to be due to bespoke templates; the more common ones can be added over time.

A word about the output: Since the structured data setup, including properties and foreign keys, is still in flux, I opted for a simple output format. It is not Wikibase format, but similar; most elements (except categories and coordinates, I think) are just lists of type-and-value tuples (example). I try to use URLs as much as possible, for example, when referencing users on Commons (or other Wikimedia projects) or flickr. Licenses are currently links to the Wikidata element corresponding to the used template (ideally, I would like to resolve that through Wikidata properties pointing to the appropriate license).

Source code is available. Pull requests are welcome.

WDQ, obsolete?

Since a few years, I run the WikiData Query tool (WDQ) to provide a query functionality to Wikidata. Nowadays, the (confusingly similarly named) SPARQL-based WDQS is the “official” way to query Wikidata. WDQS has been improving a lot, and while some of my tools still support WDQ, I deliberately left that option out of new tools like PetScan. But before I shut down WDQ, and the tools that use it, for good, I wanted to know if it is still used, and if SPARQL could take over.

I therefore added a query logger to Autolist1 and Autolist2. The logs contain all WDQ queries run through those tools. I will monitor the results for a while, but here is what I saw so far. I will comment on translating the query to SPARQL using WDQ2SPARQL, the general ability for such queries, and the performance of WDQS. “OK” means the query could be converted automatically to SPARQL, runs, and produces a similar (as in, equal or more up-to-date) result.

WDQ Comment
CLAIM[279:13219666]  OK
BETWEEN[569,1016-1,1016-12] BETWEEN not implemented in WDQS, but manual translation feasible

Update: This has been implemented by smalyshev no, runs OK!

(CLAIM[1435:10387684] OR CLAIM[1435:10387575]) AND NOCLAIM[380] AND NOCLAIM[481]  OK
BETWEEN[569,1359-1,1359-12] BETWEEN not implemented in WDQS, but manual translation feasible

Update: This has been implemented by smalyshev no, runs OK!

CLAIM[31:5]  All humans ~3.2M humans on Wikidata. Not really a useful query in these tools.
Q22686  Single item. Doesn’t really need a query?
Q22686  Single item. Doesn’t really need a query?
CLAIM[106:170790] AND CLAIM[27:35]  OK
CLAIM[195:842858]  OK
Gustav III  What the hell?
claim[17]  All items with “country”. Not really a useful query in these tools.
claim[31]  All items with “instance of”. Not really a useful query in these tools.
claim[106:82955] and claim[509:(tree[12078][][279])]  OK
claim[31:5]   All humans ~3.2M humans on Wikidata. Not really a useful query in these tools.
claim[31:5]   All humans ~3.2M humans on Wikidata. Not really a useful query in these tools.
claim[21]  All items with gender. Not really a useful query in these tools.
LINK[lvwiki] AND CLAIM[31:5]  OK
LINK[lvwiki] AND CLAIM[31:5]  OK
claim[27] and noclaim[21]  OK
LINK[lvwiki] AND CLAIM[31:56061]  OK
LINK[lvwiki] AND tree[56061][150][17,279]  OK
claim[31:(tree[16521][][279])]  OK

As far as I can tell, SPARQL could take over for WDQ immediately.