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
<markus.kroetz...@tu-dresden.de <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
<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>
(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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