Hi all,

let me send you a paper from 2013, which might either help directly or at least to get some ideas...

A lemon lexicon for DBpedia, Christina Unger, John McCrae, Sebastian Walter, Sara Winter, Philipp Cimiano, 2013, Proceedings of 1st International Workshop on NLP and DBpedia, co-located with the 12th International Semantic Web Conference (ISWC 2013), October 21-25, Sydney, Australia


Since the mappings from DBpedia to Wikidata properties are here: http://mappings.dbpedia.org/index.php?title=Special:AllPages&namespace=202 e.g. http://mappings.dbpedia.org/index.php/OntologyProperty:BirthDate

You could directly use the DBpedia-lemon lexicalisation for Wikidata.

The mappings can be downloaded with

git clone https://github.com/dbpedia/extraction-framework ; cd core ; ../run download-mappings

All the best,


On 14.01.19 18:34, Denny Vrandečić wrote:

thanks for the kind words.

There are a few research projects that use Wikidata to generate parts of Wikipedia articles - see for example https://arxiv.org/abs/1702.06235 which is almost as good as human results and beats templates by far, but only for the first sentence of biographies.

Lucie Kaffee has also quite a body of research on that topic, and has worked very succesfully and tightly with some Wikipedia communities on these questions. Here's her bibliography: https://scholar.google.com/citations?user=xiuGTq0AAAAJ&hl=de 

Another project of hers is currently under review for a grant: https://meta.wikimedia.org/wiki/Grants:Project/Scribe:_Supporting_Under-resourced_Wikipedia_Editors_in_Creating_New_Articles  - I would suggest to take a look and if you are so inclined to express support. It is totally worth it!

My opinion is that these projects are great for starters, and should be done (low-hanging fruits and all that), but won't get much further at least for a while, mostly because Wikidata rarely offers more than a skeleton of content. A decent Wikipedia article will include much, much more content than what is represented in Wikidata. And if you only use that for input, you're limiting yourself too much.

Here's a different approach based on summarization over input sources: https://www.wired.com/story/using-artificial-intelligence-to-fix-wikipedias-gender-problem/ - this has a more promising approach for the short- to mid-term.

I still maintain that the Abstract Wikipedia approach has certain advantages over both learned approaches, and is most aligned with Lucie's work. The machine learned approaches always fall short on the dimension of editability, due to the black-boxness of their solutions.

Also, furthermore, agree to Jeblad.

Remains the question, why is there not more discussion? Maybe because there is nothing substantial to discuss yet :) The two white papers are rather high level and the idea is not concrete enough yet, so that I wouldn't expect too much discussion yet going on on-wiki. That was similar to Wikidata - the number who discussed Wikidata at this level of maturity was tiny, it increased considerably once an actual design plan was suggested, but still remained small - and then exploded once the system was deployed. I would be surprised and delighted if we managed to avoid this pattern this time, but I can't do more than publicly present the idea, announce plans once they are there, and hope for a timely discussion :)


On Mon, Jan 14, 2019 at 2:54 AM John Erling Blad <jeblad@gmail.com> wrote:
An additional note; what Wikipedia urgently needs is a way to create
and reuse canned text (aka "templates"), and a way to adapt that text
to data from Wikidata. That is mostly just inflection rules, but in
some cases it involves grammar rules. To create larger pieces of text
is much harder, especially if the text is supposed to be readable.
Jumbling sentences together as is commonly done by various botscripts
does not work very well, or rather, it does not work at all.

On Mon, Jan 14, 2019 at 11:44 AM John Erling Blad <jeblad@gmail.com> wrote:
> Using an abstract language as an basis for translations have been
> tried before, and is almost as hard as translating between two common
> languages.
> There are two really hard problems, it is the implied references and
> the cultural context. An artificial language can get rid of the
> implied references, but it tend to create very weird and unnatural
> expressions. If the cultural context is removed, then it can be
> extremely hard to put it back in, and without any cultural context it
> can be hard to explain anything.
> But yes, you can make an abstract language, but it won't give you any
> high quality prose.
> On Mon, Jan 14, 2019 at 8:09 AM Felipe Schenone <schenonef@gmail.com> wrote:
> >
> > This is quite an awesome idea. But thinking about it, wouldn't it be possible to use structured data in wikidata to generate articles? Can't we skip the need of learning an abstract language by using wikidata?
> >
> > Also, is there discussion about this idea anywhere in the Wikimedia wikis? I haven't found any...
> >
> > On Sat, Sep 29, 2018 at 3:44 PM Pine W <wiki.pine@gmail.com> wrote:
> >>
> >> Forwarding because this (ambitious!) proposal may be of interest to people
> >> on other lists. I'm not endorsing the proposal at this time, but I'm
> >> curious about it.
> >>
> >> Pine
> >> ( https://meta.wikimedia.org/wiki/User:Pine )
> >>
> >>
> >> ---------- Forwarded message ---------
> >> From: Denny Vrandečić <vrandecic@gmail.com>
> >> Date: Sat, Sep 29, 2018 at 6:32 PM
> >> Subject: [Wikimedia-l] Wikipedia in an abstract language
> >> To: Wikimedia Mailing List <wikimedia-l@lists.wikimedia.org>
> >>
> >>
> >> Semantic Web languages allow to express ontologies and knowledge bases in a
> >> way meant to be particularly amenable to the Web. Ontologies formalize the
> >> shared understanding of a domain. But the most expressive and widespread
> >> languages that we know of are human natural languages, and the largest
> >> knowledge base we have is the wealth of text written in human languages.
> >>
> >> We looks for a path to bridge the gap between knowledge representation
> >> languages such as OWL and human natural languages such as English. We
> >> propose a project to simultaneously expose that gap, allow to collaborate
> >> on closing it, make progress widely visible, and is highly attractive and
> >> valuable in its own right: a Wikipedia written in an abstract language to
> >> be rendered into any natural language on request. This would make current
> >> Wikipedia editors about 100x more productive, and increase the content of
> >> Wikipedia by 10x. For billions of users this will unlock knowledge they
> >> currently do not have access to.
> >>
> >> My first talk on this topic will be on October 10, 2018, 16:45-17:00, at
> >> the Asilomar in Monterey, CA during the Blue Sky track of ISWC. My second,
> >> longer talk on the topic will be at the DL workshop in Tempe, AZ, October
> >> 27-29. Comments are very welcome as I prepare the slides and the talk.
> >>
> >> Link to the paper: http://simia.net/download/abstractwikipedia.pdf
> >>
> >> Cheers,
> >> Denny
> >> _______________________________________________
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All the best,
Sebastian Hellmann

Director of Knowledge Integration and Linked Data Technologies (KILT) Competence Center
at the Institute for Applied Informatics (InfAI) at Leipzig University
Executive Director of the DBpedia Association
Projects: http://dbpedia.org, http://nlp2rdf.org, http://linguistics.okfn.org, https://www.w3.org/community/ld4lt
Homepage: http://aksw.org/SebastianHellmann
Research Group: http://aksw.org