Thanks for making the logs available. Personally I would be interested
in knowing how often a certain item pops up in queries. That way it
would make easier to know the popularity of certain items.
Do you think it's something that could be accomplished?
This would be quite easy to do: since each query is one line in the
files, and since we have expanded all URLs (meaning they close with ">",
which is URL-encoded as "%3E"), you can simply do a zgrep -c over the
files to count the queries that mention the item (and make sure to use
the closing "%3E" to avoid Q1234 matching a search for Q123). One such
grep over any of the three larger files takes less than a minute.
If you want a sorted list of "most popular" items, this is a bit more
work and would require at least some Python script, or some less obvious
combination of sed (extracting all URLs of entities), and sort.
On Tue, 7 Aug 2018, 17:01 Markus Kroetzsch,
I am happy to announce that as part of an ongoing research
between TU Dresden researchers and Wikimedia , we could now release
pre-processed logs from the Wikidata SPARQL Query Service . You can
find details and download links on the following page:
The data so far comprises over 200 million queries answered in
June-August 2017. There is also an accompanying publication that
describes the workings of and practical experiences with the SPARQL
query service .
The logs have been pre-processed to remove information that could
potentially be used for identifying individual users (e.g., comments
were removed, geo-coordinates coarsened, and query strings reformatted
completely -- see above page for details). Nevertheless, one can still
learn many interesting things from the logs, e.g., which properties and
entities are used in queries, which SPARQL features are most prominent,
or which languages are requested.
We also have preserved some amount of user agent information, but
without overly detailed software versions and only in cases where the
agents occurred many times across several weeks. This can at least be
used to recognise the (significant amount) of queries generated, e.g.,
by Magnus' tools, or to do a rough analysis of which software platforms
are mostly used to send queries from. We used #TOOL comments found in
queries to refine user agent information in some cases.
We also made an effort to identify those queries that come from browser
agents *and* also behave like one would expect from a browser (not all
"browsers" did). We called such queries "organic" and provide
classification with the logs (there is also a filtered dump of only
organic queries, which is much smaller and therefore nicer to process,
also for testing). See the paper for details on our methodology.
Finally, the data contains the time of each request, so one can
reconstruct query loads over time.
Feedback is very welcome, both in terms of comments on the data (is it
useful to you? would you like to see more? do you have concerns?)
terms of insights that you can get from it (we did some analyses but
can surely do more).
(or rather the web service that powers
this UI and many other applications).
 Stanislav Malyshev, Markus Krötzsch, Larry González, Julius
Adrian Bielefeldt: Getting the Most out of Wikidata: Semantic
Usage in Wikipedia’s Knowledge Graph. In Proceedings of the 17th
International Semantic Web Conference (ISWC-18), Springer 2018.
Prof. Dr. Markus Kroetzsch
Knowledge-Based Systems Group
Center for Advancing Electronics Dresden (cfaed)
Faculty of Computer Science
+49 351 463 38486
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