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 https://github.com/ag-sc/lemon.dbpedia https://pdfs.semanticscholar.org/638e/b4959db792c94411339439013eef536fb052.… Since the mappings from DBpedia to Wikidata properties are here: http://mappings.dbpedia.org/index.php?title=Special:AllPages&namespace=… 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, Sebastian On 14.01.19 18:34, Denny Vrandečić wrote: Felipe, 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-res… - 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… - 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 :) Cheers, Denny On Mon, Jan 14, 2019 at 2:54 AM John Erling Blad &lt;jeblad(a)gmail.com&gt; wrote: _______________________________________________ Wikidata mailing listWikidata@lists.wikimedia.orghttps://lists.wikimedia.org/mailman/listinfo/wikidata -- 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 <http://www.w3.org/community/ld4lt> Homepage: http://aksw.org/SebastianHellmann Research Group: http://aksw.org