![Pudú: [Wikidata] Wikidata query performance paper, WUaS - "What are most impt stats issues in earth/space sci that journalists should understand?", 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](https://upload.wikimedia.org/wikipedia/commons/a/a7/Pudu_puda_01.jpg){kind=link}
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/wik
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/rei
(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. :)
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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/
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.
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
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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.
> 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.
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Hey Scott,
On 07-08-2016 16:15, Info WorldUniversity wrote:
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.
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 07-08-2016 16:15, Info WorldUniversity wrote:
Hi Aidan, Markus, Daniel and Wikidatans,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.
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).
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, (orIn 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.
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/7615938422020505 60 - 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?
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
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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 - "What are most impt stats issues in earth/space sci that journalists should understand?" - and a Qidentifier, and with rank-able outcomes, create Wikidata Q-items/identifiers with attributes possibly for each MIT OCW course in all 7 languages 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 which is inherent ill-defined/subjective, 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/
Statistics (e.g. http://ocw.mit.edu/courses/
Space/Astronautics courses (http://ocw.mit.edu/courses/
Journalism wiki subject page (e.g. http://ocw.mit.edu/courses/
... 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/
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/
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
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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.or
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.or
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On 07.08.2016 22:58, Stas Malyshev wrote:
https://docs.google.com/spread
Some criteria there are obviously rather vague and subjective, but even when disregarding the scoring, it shows which systems have been looked at.
Hi!If you want to go into Wikidata history, here is the "giant spreadsheet" Stas was referring to:
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.
https://docs.google.com/spread
Some criteria there are obviously rather vague and subjective, but even when disregarding the scoring, it shows which systems have been looked at.
Markus
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...
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