The latest release delivers better control over tagging, custom names for tags, defining new tags, Save API and OpenID support.

We are happy to announce the addition of several new features. The purpose of the new features is mainly to facilitate the use of common tags from Wikipedia, as well as to overcome Wikipedia’s limitations as a controlled vocabulary for semantic tags.

Tagging emerged as an extremely popular way to integrate and organize data, due to its simplicity and flexibility. However, free-word tags do not have defined meanings, so it isn’t always clear what a particular tag represents. Does the tag “jaguar” represent the animal, the car company, or the operating system?

On the other hand, common, “semantic” tags are unique, well-defined concepts that allow people to state what a web page is exactly about. Semantic tags come at a price, though. They reintroduce structure, the absence of which was the main reason why tagging has become so popular.

The question is: Is it possible to make semantic tags as flexible as classic ones? Can humans accept and love the format intended for machines? Today’s release is Faviki’s attempt to answer this challenge.

Features in this release include:

Enhanced tagging interface

Universal Wikipedia tags are often too long and too hard to enter and the exact name of a tag has to be known beforehand. Furthermore, tags are personal items – a private association to some concept. They are often based on emotions, for instance: the nickname “Pippo” instead of the full name of the soccer player “Filippo Inzaghi”.

The new release makes it possible to use custom names for tags. Tags are added in free form, resembling classic tagging. If Faviki doesn’t understand a tag provided by a user, it will ask her to disambiguate it. It will then remember her choice and, next time, it will know what she means.

Faviki “learns” about user’s name of the tag

Faviki “learns” about user’s name of the tag

This is possible by connecting the idea of tagging with the idea of searching. Tags are used as keywords for a Google search that is restricted to Wikipedia’s domain. After all, tags and keywords are subjective associations to unique concepts and Google search is a great way to find URLs that represent these concepts. This way, users can use keywords as custom tags for Wikipedia URLs.

In addition, custom names for tags can also be modified explicitly on the Tag page.

Defining new tags

Wikipedia is the world’s largest encyclopedia, but it still covers only a small portion of the real world. There is a large number of concepts that are either too specialized or do not possess sufficient “notability” to be included in a common encyclopedia.

We already take for granted that every company or organization has a URL and that most people we know have some kind of web page, a blog, a social network profile or a company page that represents them online.

Faviki exploited this fact in one of its new features – defining new tags. New tags are added the same way as Wikipedia tags. The difference is that, this time, Google search is not restricted to Wikipedia’s domain, but only a few of the top results are allowed to be selected. Google returns web pages from the whole Web and users collaboratively create new tags and decide which URLs are the best candidates for new concepts.

Users collaboratively decide the best URLs for a concept

Users collaboratively decide the best URLs for a concept

Save/Edit API

The Faviki Save/Edit API is a simple API that provides a way to save and edit bookmarks from other applications.

OpenID support

Faviki finally supports OpenID. It uses RPX, a service which integrates various OpenID implementations from Google, Yahoo, AOL, Microsoft, along with plain OpenID.

Other features/improvements

  • Smarter autocomplete list
    The autocomplete list is an alternative way of finding and adding tags. It is now powered by DBpedia lookup – a powerful search API for Wikipedia concepts.
  • Converting tags
    This feature allows users to convert any of their tags to another tag across all of their bookmarks.
  • Spam control
    Bookmarks of no value for users can easily be marked as spam. Bookmarks that were marked as spam by a certain number of users are hidden.
  • Export/backup bookmarks
    Bookmarks can be exported along with semantic tags in the standard HTML bookmarks format.
  • Tag description tooltip
    A short abstract with an image, if there is any, shown when a mouse is held over a tag name, helps users choose the right tag. The data is fetched from DBpedia in real time.

Thanks to all of our users who have given us the feedback regarding the new features on Faviki. Stay tuned, further information will be released on the blog soon!

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What is it?

You probably noticed the ‘G’ button on the right hand side of the field for adding new tags, and of the ‘tags’ field in the search. That is the Google search button that we wrote about on our Help page here. However, we thought that this feature deserves its own post on the blog, because it helped us with finding tags many, many times.

How does it work?

With Google search button, you can search for tags as you would search Wikipedia pages on Google. For instance, if you type in ‘apple’, and click on the Google button, the system will automatically add ‘wikipedia’, so your query will actually be ‘apple wikipedia’, and search result will be retrieved from the domain en.wikipedia.org only.

Faviki google search api button

Experience showed us that this way of finding tags can be quite helpful and time saving. Sometimes it is hard to find the most appropriate tag with autocomplete list, and Google is pretty clever when it comes to finding the most popular/representative tag for an acronym or ambiguous term, for instance. So, it is often the case that the tag that you are looking for is at the top of the list. To add it just click on the ‘copy’ link.

Cases in which it beats the autocomplete list

  • Acronyms and their disambiguation:
    • EU = European Union
    • RHCP = Red Hot Chili Peppers
    • CSS = Cascading Style Sheets, Content Scramble System, Cansei de Ser Sexy
    • LCD = Liquid crystal display, Lacida, Lowest common denominator
    • SEO = Search engine optimization, Seasoned equity offering
    • RDF = Resource Description Framework, Robotech Defense Force, Radical Dance Faction
    • REST = Representational State Transfer
  • Ambiguous terms:
    • apple (fruit, digital technology corporation, Fiona Apple, bank…)
    • keyboard (computers, music, magazine…)
    • office (software, place where you work, series, film…)
    • flash (software, superhero, photography, song…)
  • Searching for the right term for the concept:
    • programming = Computer programming;
    • baby = Infant;
    • tiredness = Fatigue (medical);
    • moonlight sonata = Piano Sonata No. 14 (Beethoven);
    • rachmaninov = Sergei Rachmaninoff. (Note that in this case the term is not even spelled correctly)
  • When you know what you think of, but you don’t know/can’t remember how to name it:
    • belarus capital = Minsk
    • eu lead body = European Council
    • kaiser chiefs singer = Ricky Wilson (British musician)
  • If you wish to search for related tags or tags concerning a broad topic:
    • online social (Social network, Social software, Online identity, OpenSocial, Virtual community, Social bookmarking, Social computing)
    • vegetarian (Vegetarianism, Vegetarian cuisine, Vegetarian Society, World Vegetarian Day, Veganism)
    • olympic games (Olympic Games, Summer Olympic Games, Winter Olympic Games, Ancient Olympic Games, Youth Olympic Games)
  • If the tag contains non-English characters, and you don’t want to deal with them:
    • roisin murphy = Róisín Murphy
    • motorhead = Motörhead

Drawbacks

  • It is slightly different than autocomplete list, e.g. you have to click on the ‘copy’ link instead of on the tag name (which is a link to a Wikipedia page)
  • Search results list will also contain some Wikipedia pages which are not tags, like pages whose names start with ‘Special:’, ‘Template:’, ‘User:’, ‘Wikipedia:’, ‘Help:’, ‘User talk:’, ‘Wikipedia talk:’, ‘Category:’. These are special Wikipedia pages and obviously cannot be used for tags, so you cannot add them.

We hope we’ll be able to fix these issues soon.

Summary

Inserting correct tags is essential for Faviki in order to use its potentials to the maximum. But finding the right tag is sometimes a bit tricky. We hope that Google search API can make your tagging easier and more accurate.

It started great…

When you put a lot of time and energy into something it sure is great to see that it was not in vain. Well, last week was exactly that way for us. From May 23rd Faviki is a featured project on Google code homepage, as a tool that uses Google AJAX search API.

Google API helps Faviki users with adding tags and searching. Sometimes it is hard to find the most appropriate tag with autocomplete list, especially in the cases of abbreviations and ambiguous terms. That is where Google search comes in handy. We’ll have more about this feature very soon.

This was a great nod to us and we were very excited and proud especially considering that we are a long time users of Google tools and services. Since then we’ve had a huge increase in visitors and Faviki started getting the attention we honestly believe it deserves.

But that was just the beginning. On May 26th an excellent article about Faviki was posted on ReadWriteWeb. The article is a great description of what Faviki is, how it is used and what problems it solves. Among other things the article concludes:

If that turns out to be true [that tags will play an increasingly important role in the structure of the web,] then Faviki represents a big step in that direction by offering a transitional service between social bookmarking and a purely semantic-based bookmarking service that would automatically know how to tag any content saved by discovering the semantic aspects already associated with that web page.

…and then got even better!

They say that when it rains it pours, but when it’s shining… well it can be really bright! Here’s more of the “sunshine” we had during last week.

For a while Faviki was present on Del.icio.us homepage and has so far been bookmarked by 207 people. A lot of other web pages that deal with Faviki have also been bookmarked.

Also, Faviki is officially a killer! A killer startup, that is. Here is a part of what Killer startups had to say about us:

Why it might be a killer
Faviki is the next generation in social bookmarking. There are a lot of implications for semantic search and semantic tagging. It makes this a lot more efficient and easier for the user.

A Rotorblog post by Arnold Zafra entitled Faviki Offers Social Bookmarking with Semantic Tagging stated the following:

Faviki has the making of a killer application. The only problem it faces right now is how to get into the social bookmarking niche with the presence of already popular del.ici.ous, magnolia and others. But sometimes, we users tend to look for something else. So, Faviki is a good alternative, if not worthy of at least a try.

There is also a very good post Faviki uses Wikipedia and DBpedia for semantic tagging. Author’s question was:

One interesting research question is whether it’s possible to combine the ease of using user-generated tags with the power of mapping them into tags in a structured or semi-structured knowledge base.

And his conclusion is:

Deriving knowledge bases from Wikipedia and using them in innovative is a very exciting topic that is sure to receive a lot of work in the coming years.

Dennis D. McDonald’s question was How Important Are Tags to You?. He notices the following:

While controlled indexing vocabularies and classifications schemes have existed for as long as indexes, catalogs, and information retrieval systems have existed, the benefits of such controlled vocabularies have been somewhat limited to professional and specialized communities or other organizations that already have a vested interest in standard ways of referring to concepts and ideas.

Once authorship and usage extend beyond such communities – which happens very easily online – it’s possible that the advantages of standardization, specialization, and specificity of tags might start to break down as profession- and knowledge-based borders are crossed.

We are really excited that Faviki has broken the language barrier. A detailed description of Faviki in Japanese can be found at http://mojix.org/2008/05/27/faviki. Two more posts about Faviki in Japanese can be found here and here. There are also posts in Chinese and Italian.

In addition, the first printed article is published in the Italian magazine Digital life. Check out the online version. Thanks, Donatella!

We are so modern we belong to the museum

But a very special type of museum it is. Faviki is now a member of Museum of Modern Betas. Even if you are not exactly a museum type you can get your dose of cutting edge bookmarking!

Faviki is also „on display” at Emily Chang – eHub, at Betadaily, at I’m Not Actually a Geek as well as at LawyerKM.

The word about Faviki has also spread on Twitter. We found two of the tweets especially cute:

my social bookmarking prayers have been answered: http://www.faviki.com/, by thebatlab (link)

and

Playing with http://faviki.com/ … Social bookmarking with DBpedia concepts instead of tags. Cool! Can’t decide if it’s Web 2.0 or Web 3.0., by cygri (link)

Finally, we would especially like to mention the very first post about Faviki. Matt was among the first Faviki users and his support has meant so much to us.

So, as you can see, last week was really great. We extend our thanks to all of the websites and people mentioned above. Also, we send thanks and best regards to all of our users. You Favikings :) give us the energy and drive to continue our quest of developing Faviki. Your feedback is always welcome, as we are trying to make our website better for you (and for us, because we use it too :)).

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