A million new tags in Faviki
September 19, 2008
Faviki is periodically synchronized with Wikipedia and now contains a little less than a million new tags - around 300.000 new English tags and 669.600 new tags in other languages! That means that currently there are 5.6 million tags in Faviki – 2.7 million English and 2.9 million tags from other 13 languages.
Since the September release and the multi-language tagging feature, you can tag in 14 different languages, and now there are 30% more non-English tags. After English, the largest languages are German (397.8K) and French (388.5K). The fastest growing languages are Italian (51.5% growth) and Polish (44.1%).
Wikipedia/DBpedia growth (values in thousands)
| Language | DBpedia 3.0* | DBpedia 3.1** | growth | growth (%) |
| English | 2400.0 | 2700.0 | 300.0 | 12.50% |
| German | 335.3 | 397.8 | 62.5 | 18.64% |
| French | 293.4 | 388.5 | 95.1 | 32.41% |
| Italian | 190.7 | 288.9 | 98.2 | 51.49% |
| Dutch | 223.0 | 288.3 | 65.3 | 29.28% |
| Polish | 179.7 | 259.0 | 79.3 | 44.13% |
| Portuguese | 178.7 | 248.3 | 69.6 | 38.95% |
| Spanish | 171.5 | 228.9 | 57.4 | 33.47% |
| Japanese | 164.6 | 202.3 | 37.7 | 22.90% |
| Russian | 117.1 | 153.6 | 36.5 | 31.17% |
| Swedish | 135.5 | 147.6 | 12.1 | 8.93% |
| Finnish | 96.1 | 115.0 | 18.9 | 19.67% |
| Norwegian | 86.9 | 104.5 | 17.6 | 20.25% |
| Chinese | 83.3 | 102.7 | 19.4 | 23.29% |
| Total (without Eng) | 2255.8 | 2925.4 | 669.6 | 29.68% |
| Total (with Eng) | 4655.8 | 5625.4 | 969.6 | 20.83% |
* Jan 08, Japanese version was built in November 2007
** Jun & July 08
Number of non-English tags (values in thousands)
Non-English tags growth
Faviki uses the information about tags from DBpedia datasets. DBpedia extracts structured data from Wikipedia, which is constantly growing. Last release – DBpedia 3.1 has been released recently, marking an increase of 27% over the previous version. The downloads are provided as N-Triples and in CSV format on this page.
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September 23, 2008 at 2:30 pm
[...] What’s the source of these results anway? SemantiFind’s recommended results seem to rely entirely on input generated by users – to add input, you need to install their toolbar and start adding labels to websites; if a website has been labeled before, you can confirm or reject existing labels. What’s nice: a label recommender (only presumably the same one that’s used for search queries) reduces ambiguity. What’s curious: You can also browse the pages you have already labeled in what they call your “catalogue” – which makes the service even more reminiscent of a bookmarking service, and which makes me wonder whether one shouldn’t possibly link this with a del.icio.us/Mr.Wong/Bibsonomy/Faviki account (Faviki would probably be the best, considering their tag recommendations are based on DBpedia, and considering that Faviki just made it past the 1 million tags mark) [...]