Re: [Wikidata] Wikidata query performance paper
Hey Scott, While I'm not sure I can help with the details of the specific example you are mentioning, but the general area you are in -- dealing with answering questions posed in natural language -- is called "Question Answering". When dealing with data in an RDF format (as per Wikidata), there's quite a lot of research done in the context of "Question Answering over Linked Data" (QALD). The methods are not 100% accurate, but given data in a structured format (like RDF), with good labels, and assuming relatively simple objective questions (like "what age is the current Italian president?") that can be answered over the data, I believe these techniques can get quite good results. One can check out the QALD evaluation series for more details on how good [1]. I'm not really in that area myself, but perhaps the keywords might be useful if you want to read more. Probably this will not be so helpful though if your focus is on using Wikidata to answer one specific question. :) Cheers, Aidan [1] http://qald.sebastianwalter.org/ On 07-08-2016 19:53, Scott MacLeod wrote: Thanks, Aidan, Stas and Wikidatans, Thanks for the feedback. While I'm not yet a SQL/SPARQL programmer, I wonder if one could make each word in the question concrete, a Qidentifier, and with rank-able outcomes, create Wikidata Q-items/identifiers with attributes possibly for each MIT OCW course in 7 languages courses and each Yale OYC courses, as well as each WUaS subject page. It's the ranking of responses that would lesson the significance of the question that is inherent ill-definition/subjectivity, I think (?) - and there might be other SQL/SPARQL related approaches to this problem too. Then hypothetically one could compare, for example, the list of MIT OCW Earth, Atmosphere and Planetary Science courses (e.g. http://ocw.mit.edu/courses/earth-atmospheric-and-planetary-sciences/ and in Spanish - http://ocw.mit.edu/courses/translated-courses/spanish/#earth-atmospheric-and-planetary-sciences and WUaS's Earth wiki subject - http://worlduniversity.wikia.com/wiki/Earth,_Atmospheric,_and_Planetary_Sciences), Statistics (e.g. http://ocw.mit.edu/courses/mathematics/ and in Spanish http://ocw.mit.edu/courses/translated-courses/spanish/#mathematics and WUaS's Statistics' wiki page http://worlduniversity.wikia.com/wiki/Statistics), Space/Astronautics courses (http://ocw.mit.edu/courses/aeronautics-and-astronautics/ and WUaS's Space wiki subject - http://worlduniversity.wikia.com/wiki/Space) with perhaps wiki-added WUaS Journalism wiki subject page (e.g. http://ocw.mit.edu/courses/comparative-media-studies-writing/ and Journalism http://worlduniversity.wikia.com/wiki/Journalism and various forms of writing at WUaS http://worlduniversity.wikia.com/wiki/writing) ... with Q items, newspaper articles and ask a variety of related questions of the results? It would be some sort of correlation of the relative rankings of these outputs in response to the queries - and which could yield results paralleling somehow Google Search results, for example. (Possible collaboration with Google Search even would increase eventually collaboration in voice on Android smartphones, and in Google group video Hangouts for ASL and other forms of sign language, for example). I haven't been able to find any Mandarin Chinese MIT OCW Statistics, Earth, Space, or Journalism courses - http://ocw.mit.edu/courses/translated-courses/traditional-chinese (accessible here http://ocw.mit.edu/courses/translated-courses/) yet, to speak of, although these MIT OCW Writing courses in Mandarin Chinese - http://ocw.mit.edu/courses/translated-courses/traditional-chinese/#comparative-media-studies-writing - could work possibly for some of these hypothetical Wikidata query performance questions I'm seeking to explore - in this "if one builds it approach." For example, and hypothetically, if there were 3 relatively recent and new MIT OCW Earth courses, and 2 new MIT OCW Statistics courses, and 10 journalism articles from best newspapers and best academic journals in English on Earth/Space (http://ocw.mit.edu/courses/aeronautics-and-astronautics/), and 4 in Chinese, and 5 in Spanish, for example, perhaps one could get helpful and useful outputs (that could eventually be asked for in voice/natural language processing), - by ranking relative importance partly according to the newness of the course, and getting objective relative outcomes as a group. The importance of a specific set of journals to a specific discipline / subject could be another source of ranking of importance, for example - to highlight the operative item in this question, and add some further relative rankings as useful SQL coding possibilities. Wikidata would generate or get a lot of valuable new fact-oriented and knowledge-oriented Q items/identifiers/attributes (for CC MIT OCW's 2300 courses in English, and the other courses in 6 other languages, and CC Yale OYC, as well as CC WUaS subjects, and with
Re: [Wikidata] Wikidata query performance paper
Thanks, Aidan, Stas and Wikidatans, Thanks for the feedback. While I'm not yet a SQL/SPARQL programmer, I wonder if one could make each word in the question concrete, a Qidentifier, and with rank-able outcomes, create Wikidata Q-items/identifiers with attributes possibly for each MIT OCW course in 7 languages courses and each Yale OYC courses, as well as each WUaS subject page. It's the ranking of responses that would lesson the significance of the question that is inherent ill-definition/subjectivity, I think (?) - and there might be other SQL/SPARQL related approaches to this problem too. Then hypothetically one could compare, for example, the list of MIT OCW Earth, Atmosphere and Planetary Science courses (e.g. http://ocw.mit.edu/courses/earth-atmospheric-and-planetary-sciences/ and in Spanish - http://ocw.mit.edu/courses/translated-courses/spanish/#earth-atmospheric-and-planetary-sciences and WUaS's Earth wiki subject - http://worlduniversity.wikia.com/wiki/Earth,_Atmospheric,_and_Planetary_Sciences ), Statistics (e.g. http://ocw.mit.edu/courses/mathematics/ and in Spanish http://ocw.mit.edu/courses/translated-courses/spanish/#mathematics and WUaS's Statistics' wiki page http://worlduniversity.wikia.com/wiki/Statistics), Space/Astronautics courses ( http://ocw.mit.edu/courses/aeronautics-and-astronautics/ and WUaS's Space wiki subject - http://worlduniversity.wikia.com/wiki/Space) with perhaps wiki-added WUaS Journalism wiki subject page (e.g. http://ocw.mit.edu/courses/comparative-media-studies-writing/ and Journalism http://worlduniversity.wikia.com/wiki/Journalism and various forms of writing at WUaS http://worlduniversity.wikia.com/wiki/writing) ... with Q items, newspaper articles and ask a variety of related questions of the results? It would be some sort of correlation of the relative rankings of these outputs in response to the queries - and which could yield results paralleling somehow Google Search results, for example. (Possible collaboration with Google Search even would increase eventually collaboration in voice on Android smartphones, and in Google group video Hangouts for ASL and other forms of sign language, for example). I haven't been able to find any Mandarin Chinese MIT OCW Statistics, Earth, Space, or Journalism courses - http://ocw.mit.edu/courses/translated-courses/traditional-chinese (accessible here http://ocw.mit.edu/courses/translated-courses/) yet, to speak of, although these MIT OCW Writing courses in Mandarin Chinese - http://ocw.mit.edu/courses/translated-courses/traditional-chinese/#comparative-media-studies-writing - could work possibly for some of these hypothetical Wikidata query performance questions I'm seeking to explore - in this "if one builds it approach." For example, and hypothetically, if there were 3 relatively recent and new MIT OCW Earth courses, and 2 new MIT OCW Statistics courses, and 10 journalism articles from best newspapers and best academic journals in English on Earth/Space ( http://ocw.mit.edu/courses/aeronautics-and-astronautics/), and 4 in Chinese, and 5 in Spanish, for example, perhaps one could get helpful and useful outputs (that could eventually be asked for in voice/natural language processing), - by ranking relative importance partly according to the newness of the course, and getting objective relative outcomes as a group. The importance of a specific set of journals to a specific discipline / subject could be another source of ranking of importance, for example - to highlight the operative item in this question, and add some further relative rankings as useful SQL coding possibilities. Wikidata would generate or get a lot of valuable new fact-oriented and knowledge-oriented Q items/identifiers/attributes (for CC MIT OCW's 2300 courses in English, and the other courses in 6 other languages, and CC Yale OYC, as well as CC WUaS subjects, and with planning for major universities with these and growing number of wiki subjects in all languages). I have no idea yet how to write the SQL/SPARQL for this, but rankable Q* identifiers, new Q* identifiers and Google would be places I'd begin if I did. What do you think? Cheers, Scott On Sun, Aug 7, 2016 at 2:02 PM, Aidan Hogan wrote: > Hey Scott, > > On 07-08-2016 16:15, Info WorldUniversity wrote: > >> Hi Aidan, Markus, Daniel and Wikidatans, >> >> As an emergence out of this conversation on Wikidata query performance, >> and re cc World University and School/Wikidata, as a theoretical >> challenge, how would you suggest coding WUaS/Wikidata initially to be >> able to answer this question - "What are most impt stats issues in >> earth/space sci that journalists should understand?" - >> https://twitter.com/ReginaNuzzo/status/761179359101259776 - in many >> Wikipedia languages including however in American Sign Language (and >> other sign languages), as well as eventually in voice. (Regina Nuzzo is >> an associate Professor at Gallaudet University for the hearing >> impaired/deafne
Re: [Wikidata] Wikidata query performance paper
Hey Scott, On 07-08-2016 16:15, Info WorldUniversity wrote: Hi Aidan, Markus, Daniel and Wikidatans, As an emergence out of this conversation on Wikidata query performance, and re cc World University and School/Wikidata, as a theoretical challenge, how would you suggest coding WUaS/Wikidata initially to be able to answer this question - "What are most impt stats issues in earth/space sci that journalists should understand?" - https://twitter.com/ReginaNuzzo/status/761179359101259776 - in many Wikipedia languages including however in American Sign Language (and other sign languages), as well as eventually in voice. (Regina Nuzzo is an associate Professor at Gallaudet University for the hearing impaired/deafness, and has a Ph.D. in statistics from Stanford; Regina was born with hearing loss herself). I fear we are nowhere near answering these sorts of questions (by we, I mean the computer science community, not just Wikidata). The main problem is that the question is inherently ill-defined/subjective: there is no correct answer here. We would need to think about refining the question to something that is well-defined/objective, which even as a human is difficult. Perhaps we could consider a question such as: "what statistical methods (from a fixed list) have been used in scientific papers referenced by news articles have been published in the past seven years by media companies that have their headquarters in the US?". Of course even then, there are still some minor subjective aspects, and Wikidata would not have coverage, to answer such a question. The short answer is that machines are nowhere near answering these sorts of questions, no more than we are anywhere near taking a raw stream of binary data from an .mp4 video file and turning it into visual output. If we want to use machines to do useful things, we need to meet machines half-way. Part of that is formulating our questions in a way that machines can hope to process. I'm excited for when we can ask WUaS (or Wikipedia) this question, (or so many others) in voice combining, for example, CC WUaS Statistics, Earth, Space & Journalism wiki subject pages (with all their CC MIT OCW and Yale OYC) - http://worlduniversity.wikia.com/wiki/Subjects - in all of Wikipedia's 358 languages, again eventually in voice and in ASL/other sign languages (https://twitter.com/WorldUnivAndSch/status/761593842202050560 - see, too - https://www.wikidata.org/wiki/Wikidata:Project_chat#Schools). Thanks for your paper, Aidan, as well. Would designing for deafness inform how you would approach "Querying Wikidata: Comparing SPARQL, Relational and Graph Databases" in any new ways? In the context of Wikidata, the question of language is mostly a question of interface (which is itself non-trivial). But to answer the question in whatever language or mode, the question first has to be answered in some (machine-friendly) language. This is the direction in which Wikidata goes: answers are first Q* identifiers, for which labels in different languages can be generated and used to generate a mode. Likewise our work is on the level of generating those Q* identifiers, which can be later turned into tables, maps, sentences, bubbles, etc. I think the interface question is an important one, but a different one to that which we tackle. Cheers, Aidan On Sat, Aug 6, 2016 at 12:29 PM, Markus Kroetzsch mailto:markus.kroetz...@tu-dresden.de>> wrote: Hi Aidan, Thanks, very interesting, though I have not read the details yet. I wonder if you have compared the actual query results you got from the different stores. As far as I know, Neo4J actually uses a very idiosyncratic query semantics that is neither compatible with SPARQL (not even on the BGP level) nor with SQL (even for SELECT-PROJECT-JOIN queries). So it is difficult to compare it to engines that use SQL or SPARQL (or any other standard query language, for that matter). In this sense, it may not be meaningful to benchmark it against such systems. Regarding Virtuoso, the reason for not picking it for Wikidata was the lack of load-balancing support in the open source version, not the performance of a single instance. Best regards, Markus On 06.08.2016 18:19, Aidan Hogan wrote: Hey all, Recently we wrote a paper discussing the query performance for Wikidata, comparing different possible representations of the knowledge-base in Postgres (a relational database), Neo4J (a graph database), Virtuoso (a SPARQL database) and BlazeGraph (the SPARQL database currently in use) for a set of equivalent benchmark queries. The paper was recently accepted for presentation at the International Semantic Web Conference (ISWC) 2016. A pre-print is available here: http://aidanhogan.com/docs/wikidata-sparql-relational-graph.pdf
Re: [Wikidata] Wikidata query performance paper
Hi! > the area for a long time). I guess the more difficult question then, is, > which RDF/SPARQL implementation to choose (since any such implementation > should cover as least points 1, 2 and 4 in a similar way), which in turn > reduces down to the distinguishing questions of performance, licensing, > distribution, maturity, tech support, development community, and > non-standard features (keyword search), etc. We indeed had a giant spreadsheet in which a dozen of potential solutions (some of them were eliminated very early, but some put up a robust fight :) were evaluated on about 50 criteria. Of course, some of them were hard to formalize, and some number were a bit arbitrary, but that's what we did and Blazegraph came out with the best score. -- Stas Malyshev smalys...@wikimedia.org ___ Wikidata mailing list Wikidata@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/wikidata
Re: [Wikidata] Wikidata query performance paper
Hi Aidan, Markus, Daniel and Wikidatans, As an emergence out of this conversation on Wikidata query performance, and re cc World University and School/Wikidata, as a theoretical challenge, how would you suggest coding WUaS/Wikidata initially to be able to answer this question - "What are most impt stats issues in earth/space sci that journalists should understand?" - https://twitter.com/ReginaNuzzo/status/761179359101259776 - in many Wikipedia languages including however in American Sign Language (and other sign languages), as well as eventually in voice. (Regina Nuzzo is an associate Professor at Gallaudet University for the hearing impaired/deafness, and has a Ph.D. in statistics from Stanford; Regina was born with hearing loss herself). I'm excited for when we can ask WUaS (or Wikipedia) this question, (or so many others) in voice combining, for example, CC WUaS Statistics, Earth, Space & Journalism wiki subject pages (with all their CC MIT OCW and Yale OYC) - http://worlduniversity.wikia.com/wiki/Subjects - in all of Wikipedia's 358 languages, again eventually in voice and in ASL/other sign languages (https://twitter.com/WorldUnivAndSch/status/761593842202050560 - see, too - https://www.wikidata.org/wiki/Wikidata:Project_chat#Schools). Thanks for your paper, Aidan, as well. Would designing for deafness inform how you would approach "Querying Wikidata: Comparing SPARQL, Relational and Graph Databases" in any new ways? Best, Scott On Sat, Aug 6, 2016 at 12:29 PM, Markus Kroetzsch < markus.kroetz...@tu-dresden.de> wrote: > Hi Aidan, > > Thanks, very interesting, though I have not read the details yet. > > I wonder if you have compared the actual query results you got from the > different stores. As far as I know, Neo4J actually uses a very > idiosyncratic query semantics that is neither compatible with SPARQL (not > even on the BGP level) nor with SQL (even for SELECT-PROJECT-JOIN queries). > So it is difficult to compare it to engines that use SQL or SPARQL (or any > other standard query language, for that matter). In this sense, it may not > be meaningful to benchmark it against such systems. > > Regarding Virtuoso, the reason for not picking it for Wikidata was the > lack of load-balancing support in the open source version, not the > performance of a single instance. > > Best regards, > > Markus > > > > On 06.08.2016 18:19, Aidan Hogan wrote: > >> Hey all, >> >> Recently we wrote a paper discussing the query performance for Wikidata, >> comparing different possible representations of the knowledge-base in >> Postgres (a relational database), Neo4J (a graph database), Virtuoso (a >> SPARQL database) and BlazeGraph (the SPARQL database currently in use) >> for a set of equivalent benchmark queries. >> >> The paper was recently accepted for presentation at the International >> Semantic Web Conference (ISWC) 2016. A pre-print is available here: >> >> http://aidanhogan.com/docs/wikidata-sparql-relational-graph.pdf >> >> Of course there are some caveats with these results in the sense that >> perhaps other engines would perform better on different hardware, or >> different styles of queries: for this reason we tried to use the most >> general types of queries possible and tried to test different >> representations in different engines (we did not vary the hardware). >> Also in the discussion of results, we tried to give a more general >> explanation of the trends, highlighting some strengths/weaknesses for >> each engine independently of the particular queries/data. >> >> I think it's worth a glance for anyone who is interested in the >> technology/techniques needed to query Wikidata. >> >> Cheers, >> Aidan >> >> >> P.S., the paper above is a follow-up to a previous work with Markus >> Krötzsch that focussed purely on RDF/SPARQL: >> >> http://aidanhogan.com/docs/reification-wikidata-rdf-sparql.pdf >> >> (I'm not sure if it was previously mentioned on the list.) >> >> P.P.S., as someone who's somewhat of an outsider but who's been watching >> on for a few years now, I'd like to congratulate the community for >> making Wikidata what it is today. It's awesome work. Keep going. :) >> >> ___ >> Wikidata mailing list >> Wikidata@lists.wikimedia.org >> https://lists.wikimedia.org/mailman/listinfo/wikidata >> > > > ___ > Wikidata mailing list > Wikidata@lists.wikimedia.org > https://lists.wikimedia.org/mailman/listinfo/wikidata > -- - Scott MacLeod - Founder & President - http://worlduniversityandschool.org - 415 480 4577 - PO Box 442, (86 Ridgecrest Road), Canyon, CA 94516 - World University and School - like Wikipedia with best STEM-centric OpenCourseWare - incorporated as a nonprofit university and school in California, and is a U.S. 501 (c) (3) tax-exempt educational organization. World University and School is sending you this because of your interest in free, online, higher education. If you don't want
Re: [Wikidata] Wikidata query performance paper
Hey Daniel, On 07-08-2016 7:03, Daniel Kinzler wrote: Hi Aidan! Thank you for this very interesting research! Query performance was of course on of the key factors for selecting the technology to use for the query services. However, it was only one among several more. The Wikidata use case is different from most common scenarios in some ways, for instance: * We cannot optimize for specific queries, since users are free to submit any query they like. * The data representation needs to be intuitive enough for (thenically inclined) casual users to grasp and write queries. * The data doesn't hold still, it needs to be updated continuously, mutliple times per second. * Our data types are more complex than usual - for instance, we suppor tmultiple calendar models fro dates, and not only values but also different accuracies up to billions of years; we use "quantities" with unit and uncertainty instead of plain numbers, etc. My point is that, if we had a static data set and a handful of known queries to optimize for, we could have set up a relational or graph database that would be far more performant than what we have now. The big advantage of Blazegraph is its felxibility, not raw performance. Understood. :) Taking everything into account as mentioned above, and based on our own experiences with various experiments in the context of Wikidata and other works, I think the choice to use RDF/SPARQL was the right one (though I would be biased on this issue since I've worked in the area for a long time). I guess the more difficult question then, is, which RDF/SPARQL implementation to choose (since any such implementation should cover as least points 1, 2 and 4 in a similar way), which in turn reduces down to the distinguishing questions of performance, licensing, distribution, maturity, tech support, development community, and non-standard features (keyword search), etc. Based on raw query performance, based personally on what I have seen, I think Virtuoso probably has the lead at the moment in that it has consistently outperformed other SPARQL engines, not only in our Wikidata experiments, but in other benchmarks by other authors. However, taking all the other points into account, particularly in terms of licensing, Blazegraph does seem to have been a sound choice. And the current query service does seem to be a sound base to work forward from. It might be interesting to you to know that we initially started to implement the query service against a graph database, Titan - which was discontinued while we were still getting up to speed. Luckily this happened early on, it would have been quite painful to switch after we had gone live. This is indeed good to know! (We considered other graph database engines, but we did not think Gremlin was a good fit with what Wikidata was trying to achieve in the sense of being too "imperative": though one can indeed do something like bgps with the language, it's not particularly easy, nor intuitive.) Cheers, Aidan Am 06.08.2016 um 18:19 schrieb Aidan Hogan: Hey all, Recently we wrote a paper discussing the query performance for Wikidata, comparing different possible representations of the knowledge-base in Postgres (a relational database), Neo4J (a graph database), Virtuoso (a SPARQL database) and BlazeGraph (the SPARQL database currently in use) for a set of equivalent benchmark queries. The paper was recently accepted for presentation at the International Semantic Web Conference (ISWC) 2016. A pre-print is available here: http://aidanhogan.com/docs/wikidata-sparql-relational-graph.pdf Of course there are some caveats with these results in the sense that perhaps other engines would perform better on different hardware, or different styles of queries: for this reason we tried to use the most general types of queries possible and tried to test different representations in different engines (we did not vary the hardware). Also in the discussion of results, we tried to give a more general explanation of the trends, highlighting some strengths/weaknesses for each engine independently of the particular queries/data. I think it's worth a glance for anyone who is interested in the technology/techniques needed to query Wikidata. Cheers, Aidan P.S., the paper above is a follow-up to a previous work with Markus Krötzsch that focussed purely on RDF/SPARQL: http://aidanhogan.com/docs/reification-wikidata-rdf-sparql.pdf (I'm not sure if it was previously mentioned on the list.) P.P.S., as someone who's somewhat of an outsider but who's been watching on for a few years now, I'd like to congratulate the community for making Wikidata what it is today. It's awesome work. Keep going. :) ___ Wikidata mailing list Wikidata@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/wikidata ___ Wikidata mailing list Wikidata@lists.wikimedia.org https://lis
Re: [Wikidata] Wikidata query performance paper
Hi Aidan! Thank you for this very interesting research! Query performance was of course on of the key factors for selecting the technology to use for the query services. However, it was only one among several more. The Wikidata use case is different from most common scenarios in some ways, for instance: * We cannot optimize for specific queries, since users are free to submit any query they like. * The data representation needs to be intuitive enough for (thenically inclined) casual users to grasp and write queries. * The data doesn't hold still, it needs to be updated continuously, mutliple times per second. * Our data types are more complex than usual - for instance, we suppor tmultiple calendar models fro dates, and not only values but also different accuracies up to billions of years; we use "quantities" with unit and uncertainty instead of plain numbers, etc. My point is that, if we had a static data set and a handful of known queries to optimize for, we could have set up a relational or graph database that would be far more performant than what we have now. The big advantage of Blazegraph is its felxibility, not raw performance. It might be interesting to you to know that we initially started to implement the query service against a graph database, Titan - which was discontinued while we were still getting up to speed. Luckily this happened early on, it would have been quite painful to switch after we had gone live. -- daniel Am 06.08.2016 um 18:19 schrieb Aidan Hogan: > Hey all, > > Recently we wrote a paper discussing the query performance for Wikidata, > comparing different possible representations of the knowledge-base in Postgres > (a relational database), Neo4J (a graph database), Virtuoso (a SPARQL > database) > and BlazeGraph (the SPARQL database currently in use) for a set of equivalent > benchmark queries. > > The paper was recently accepted for presentation at the International Semantic > Web Conference (ISWC) 2016. A pre-print is available here: > > http://aidanhogan.com/docs/wikidata-sparql-relational-graph.pdf > > Of course there are some caveats with these results in the sense that perhaps > other engines would perform better on different hardware, or different styles > of > queries: for this reason we tried to use the most general types of queries > possible and tried to test different representations in different engines (we > did not vary the hardware). Also in the discussion of results, we tried to > give > a more general explanation of the trends, highlighting some > strengths/weaknesses > for each engine independently of the particular queries/data. > > I think it's worth a glance for anyone who is interested in the > technology/techniques needed to query Wikidata. > > Cheers, > Aidan > > > P.S., the paper above is a follow-up to a previous work with Markus Krötzsch > that focussed purely on RDF/SPARQL: > > http://aidanhogan.com/docs/reification-wikidata-rdf-sparql.pdf > > (I'm not sure if it was previously mentioned on the list.) > > P.P.S., as someone who's somewhat of an outsider but who's been watching on > for > a few years now, I'd like to congratulate the community for making Wikidata > what > it is today. It's awesome work. Keep going. :) > > ___ > Wikidata mailing list > Wikidata@lists.wikimedia.org > https://lists.wikimedia.org/mailman/listinfo/wikidata -- Daniel Kinzler Senior Software Developer Wikimedia Deutschland Gesellschaft zur Förderung Freien Wissens e.V. ___ Wikidata mailing list Wikidata@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/wikidata
