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-an… and WUaS's Earth wiki subject - http://worlduniversity.wikia.com/wiki/Earth,_Atmospheric,_and_Planetary_Sci…), 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/#comparat… - 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 &lt;aidhog(a)gmail.com <mailto:aidhog@gmail.com>> 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 <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 <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 <https://twitter.com/WorldUnivAndSch/status/761593842202050560> - see, too - https://www.wikidata.org/wiki/Wikidata:Project_chat#Schools <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 &lt;markus.kroetzsch(a)tu-dresden.de <mailto:markus.kroetzsch@tu-dresden.de> <mailto:markus.kroetzsch@tu-dresden.de <mailto:markus.kroetzsch@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 <http://aidanhogan.com/docs/wikidata-sparql-relational-graph.pdf> <http://aidanhogan.com/docs/wikidata-sparql-relational-graph.pdf <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 <http://aidanhogan.com/docs/reification-wikidata-rdf-sparql.pdf> <http://aidanhogan.com/docs/reification-wikidata-rdf-sparql.pdf <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. 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