http://faviki.wordpress.com/2008/09/19/almost-1-million-new-tags-added/ The official blog of Faviki, a social bookmarking tool based on semantic Wikipedia tags. Thu, 24 Sep 2009 23:33:53 +0000 http://wordpress.com/ hourly 1 http://faviki.wordpress.com/2008/09/19/almost-1-million-new-tags-added/#comment-54 The Semantic Puzzle | Extending Google: First Look at SemantiFind Tue, 23 Sep 2008 13:30:19 +0000 http://faviki.wordpress.com/?p=93#comment-54 [...] 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) [...] [...] 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) [...]

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