Aggregates over BTC2010 namespaces

Yesterday I dumped the most basic BTC2010 stats. Today I have processed them a bit more – and it gets slightly less boring.

First predicates, yesterday I had the raw count per predicate. Much more interesting is the namespaces the predicates are defined in. These are the top 10:

#triples namespace
860,532,348 rdfs
651,432,324 http://data-gov.tw.rpi.edu/vocab/p/90
588,063,466 rdf
527,347,381 gr
284,679,897 foaf
44,119,248 dc11
41,961,046 http://purl.uniprot.org/core
17,233,778 rss
13,661,605 http://www.proteinontology.info/po.owl
13,009,685 owl

(prefix abbreviations are made from prefix.cc \u2013 I am too lazy to fix the missing ones)

Now it gets interesting – because I did exactly this last year as well, and now we can compare!

Dropouts

In 2009 there were 3,817 different namespaces, this year we have 3,911, but actually only 2,945 occur in both. The biggest dropouts, i.e. namespaces that occurred last year, but not at all this year are:

#triples namespace
10,239,809 http://www.kisti.re.kr/isrl/ResearchRefOntology
5,443,549 nie
1,571,547 http://ontologycentral.com/2009/01/eurostat/ns
1,094,963 http://sindice.com/exfn/0.1
320,155 http://xmdr.org/ont/iso11179-3e3draft_r4.owl
307,534 http://cb.semsol.org/ns
242,427 nco
203,283 osag
187,600 http://auswiki.org/index.php/Special:URIResolver
159,536 nexif

I am of course shocked and saddened to see that the Nepomuk Information Elements ontology has fallen out of fashion all together, although it was a bit of a freak occurrence last year. I am not sure how we lost 10M research ontology triples?

Newcomers

Looking the other way around, what namespaces are new and popular this year, we get:

#triples namespace
651,432,324 http://data-gov.tw.rpi.edu/vocab/p/90
5,001,909 fec
2,689,813 http://transport.data.gov.uk/0/ontology/traffic
543,835 http://rdf.geospecies.org/ont/geospecies
526,304 http://data-gov.tw.rpi.edu/vocab/p/401
469,446 http://data-gov.tw.rpi.edu/2009/data-gov-twc.rdf
446,120 http://education.data.gov.uk/def/school
223,726 http://www.w3.org/TR/rdf-schema
190,890 http://wecowi.de/wiki/Spezial:URIResolver
166,511 http://data-gov.tw.rpi.edu/vocab/p/10

Here the introduction of data.gov and data.gov.uk were the big events last year.

Winners

For the namespaces that occurred both years we can find the biggest gainers. Here I calculated what ratio of the total triples each namespace constituted each year, and the increase in this ratio from 2009 to 2010. For example, GoodRelations, on top here, constituted nearly 16% of all triples in 2010, but only 2.91e-4% of all triples last year, for a cool increase of 570,000% :)

gain namespace
57058.38 gr
2636.34 http://www.openlinksw.com/schema/attribution
2182.81 http://www.openrdf.org/schema/serql
1944.68 http://www.w3.org/2007/OWL/testOntology
1235.02 http://referata.com/wiki/Special:URIResolver
1211.35 urn:lsid:ubio.org:predicates:recordVersion
1208.09 urn:lsid:ubio.org:predicates:lexicalStatus
1194.66 urn:lsid:lsid.zoology.gla.ac.uk:predicates:mapping
1191.39 urn:lsid:lsid.zoology.gla.ac.uk:predicates:rank
701.66 urn:lsid:ubio.org:predicates:hasCAVConcept

Losers

Similarly, we have the biggest losers, the ones who lost the most:

gain namespace
0.000185 http://purl.org/obo/metadata
0.000191 sioct
0.000380 vcard
0.000418 affy
0.000438 http://www.geneontology.org/go
0.000677 http://tap.stanford.edu/data
0.000719 urn://wymiwyg.org/knobot/default
0.000787 akts
0.000876 http://wymiwyg.org/ontologies/language-selection
0.000904 http://wymiwyg.org/ontologies/knobot

If your namespace is a loser, do not worry, remember that BTC is a more or less arbitrary snapshot of SOME semantic web data, and you can always catch up next year! :)

With a bit of luck I will do this again for the Pay-Level-Domains for the context URLs tomorrow.

Update

(a bit later)

You can get the full datasets for this from many eyes:

 

5 comments.

  1. [...] This post was mentioned on Twitter by Gunnar Grimnes, martin hepp. martin hepp said: Nice statistics: #goodrelations accounts for 16 % of all triples on the web & rose by 570.000 % compared to 2009: http://bit.ly/9zJDoa #lod [...]

  2. Hi Gunnar,

    nice stats… as for the triple stats, did you actually use unique triples or quads? kaufkauf.net was publishing a the same small set of triples under many URIs, which could be the reason for the inflated GoodRelations numbers (http://groups.google.com/group/pedantic-web/browse_thread/thread/ec03de1159eb5697/2d59d0ac5f6b4220).

    Best regards,
    Andreas.

  3. Andreas,

    This is counting number of quads a predicate occurs in, so if kaufkauf.net publishes the same triple in two different contexts, it is counted twice. I guess removing the 4th context part, removing duplicates and doing the analysis again would be very interesting. I shall see if I find the time!

    Cheers!

  4. [...] a comment here, Andreas Harth mentions that kaufkauf.net publishes the same triples in many contexts, and that [...]

  5. Andreas, I looked into this a bit more and put it here: http://gromgull.net/blog/2010/09/redundancy-in-the-btc2010-data-its-only-1-1b-triples/

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