David Kuhta wrote a new blog post titled Semantic Web In Action – Faviki.  He explains in great detail what Faviki does, how Faviki works, how it can help users and finally how it is used. Here are some arguments on why David recommends Faviki:

  • Simplicity – Tagging a bookmark in Faviki becomes especially simple due to to the semantic tag recommendation and search systems and concise user interface.
  • Social Media – Integration with Twitter gives you the an easy option to share bookmarks on Twitter and let your followers know where you’re leaving Web placeholders.
  • Eliminate Ambiguity – Semantic tagging means the tag you you’ve placed on a bookmark is backed by a clear and comprehensive concept in Wikipedia.
  • Eliminate Redundancy – The ability to import bookmarks from Delicious means you don’t have to switch tools or bookmark twice or change tools or bookmark again.
  • Power Search – Searching for a keyword on the Faviki homepage essentially amounts to a search of  all tagged Web resources on a given Wikipedia entry, as deemed by the collective Faviki community

Highly recommend read, especially for newcomers. Click here to read the post.

New blog posts on Faviki

January 14, 2010

Henry Story wrote a great post titled Faviki: social bookmarking for 2010. He wrote about how Faviki is different from other bookmarking services and explained the benefit users gain from using Wikipedia concepts for tagging. In addition, he discusses the potential of integrating Faviki with Linked data and Wikipedia:

Imagine you tag a page with http://dbpedia.org/resource/Munich (the user does not see this URL of course!). Then by using the growing linked data cloud Faviki or other services will be able to start doing some very interesting inferencing on this data. So since the above resource is known to be a town, a capital, to be in Germany which is in Europe, to have more than half a million inhabitants, to be along a certain river, that contains certain museums, to have different names in a number of other languages, to be related in certain ways to certain famous people (such as the current Pope)… it will be possible to improve the service to allow you to search for things you tagged with some European town (if you can’t remember where you were exactly when you took that photo).

Read the whole post here.

Also, Faviki has been covered on MakeUseOf.com. An insightful post is titled Auto-Tag Delicious Bookmarks and Share Them Easily On Twitter With Faviki. Its author, Mahendra Palsule, explains in great detail how Faviki is used, describing the usage of bookmarklet, tagging process, auto-posting to Delicious and Twitter and the import from Delicious. Here are some arguments on why Mahendra recommends Faviki:

  • Faviki uses universal, common tags, that have Wikipedia-defined meanings. Your world of knowledge captured in your bookmarks is universally connected and discoverable to your friends via these semantic tags.
  • Tagging is simple – Faviki suggests tags automatically, also allowing you to clarify exactly what you mean.
  • You do not need to switch from Delicious. You can import all your bookmarks from Delicious. Bookmarks with semantic-rich information saved in Faviki will automatically be saved in Delicious as well.
  • Automatically share your bookmarks via Twitter.
  • Multi-language Support: Faviki is the world’s first and only bookmarking service that supports tagging in 15 languages.

Highly recommend read, especially for newcomers. Click here to read the post.

Auto-Tag Delicious Bookmarks and Share Them Easily On Twitter With FavikiAuto-Tag Delicious Bookmarks and Share Them Easily On Twitter With Faviki

We are happy to announce the new import feature that will allow Faviki users to import their bookmarks from Delicious and convert free-word tags to Common tags!

The import process is semi-automatic. Before the import begins, the most frequently used Delicious tags are shown with their suggested Common tag equivalents. Users can confirm appropriate suggestions and make corrections – the more tags are reviewed, the better.

During the import process, remaining tags are either converted automatically or left unresolved. Unresolved tags will be colored red. They can be used for search but they will not be shown in the tag cloud. Unresolved tags don’t have to be defined right away: they can be defined the next time they are used for tagging but there is also an option to define them explicitly.

To import tags or turn on automatic posting to Delicious or Twitter, you have to add your service accounts to Faviki:

To add a Delicious account, go to the “Edit profile” page and click on “Delicious account settings“. After you have entered your account information, if you want all your bookmarks from Faviki to be saved to Delicious, you can turn on the “Save my bookmarks to Delicious” option and adjust tag options.

To import bookmarks from Delicious, click on the “Import Bookmarks” link. You will be able to review suggestions for most frequently used tags before the import begins.

To add a Twitter account, go to the “Edit profile” page and click on the “Twitter account settings” link. Upon entering your account information, you can turn on the “Save my bookmarks to Twitter” option and adjust your tag options. This way, all your public bookmarks will be automatically posted to your Twitter account.

In the previous release of Faviki, Common tagging has become easier by giving users the possibility to map freely labeled tags to Common tags. Today’s feature is another step toward the same goal – making Faviki easier to integrate with other services. After the import, Faviki users can keep on bookmarking using the tags they are already used to – now mapped to semantically enriched Common tags. By turning on automatic posting to Delicious and Twitter, they don’t even have to leave behind their old services.

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today announced a new import feature. It allows users to import their bookmarks from Delicious bookmarking service and convert their free-word tags to Common tags.

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I had a very interesting conversation with Jenny Zaino from SemanticWeb.com a few days ago. We were talking about the idea of semantic tagging, our participation in creating CommonTag format, the role of Wikipedia and Google in developing the Semantic Web, as well as about future plans for Faviki.

We were also chatting about new features from the Faviki last release – the possibility for users to use their own names of tags and map them to semantic tags, as well as letting them to create new tags outside of Wikipedia with help of Google search.

You can read the article here.

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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Common Tag LogoAs strong believers in the semantic tagging (we wrote about it here and here), we are happy to announce that today one big  step toward realization of the idea is made.

Faviki is involved in the development of the  new open tagging format – Common Tag, together with AdaptiveBlue, DERI (NUI Galway), Freebase, Yahoo!, Zemanta, and Zigtag. This is the first time that this number of web companies have stepped together from day one to introduce a tagging standard.

People use tags to organize, share and discover content on the Web. However, in the absence of a common tagging format, the benefits of tagging have been limited. Individual things like New York City are often represented by multiple tags (like “nyc”, “new_york_city”, and “newyork”), making it difficult to organize related content; and it is not always clear what a particular tag represents – does the tag “orange” represent the fruit or the color?

The Common Tag format was developed to address the current shortcomings of tagging and help everyone, including end users, publishers, and developers get more out of Web content. It is an outcome of an effort to develop the easiest way to let publishers get more out of their content by semantically marking it up.

Common Tag format is based on RDFa, a standard mechanism for placing structured content within HTML documents. The format uses the URIs of concepts defined on the Web as a way of anchoring the meaning of Tag objects. Common concepts can be found, among others, in two big databases of structured content (or controlled vocabularies, as librarians call it) – Freebase and DBpedia.

Common Tag is based on a small vocabulary defining:

  • A class Tag, which holds the metadata provided by a Common Tag for a specific Resource.
  • Two properties:
    • tagged (connects a document to the Tag)
    • means (connects the Tag to the concept’s URI)

There are also few subclasses and optional properties, you can have a look at the whole specification. Also, developers may feel free to make use of RDFa’s flexibility to extend the expressiveness of the Common Tag format.

An example of two tags indicating that the document is about Twitter (DBpedia URI) and Web 2.0 (Freebase URI):

<body xmlns:ctag="http://commontag.org/ns#" rel="ctag:tagged">
    <span typeof="ctag:Tag"
              rel="ctag:means" resource="http://dbpedia/resource/Twitter" />

    <span typeof="ctag:Tag"
              rel="ctag:means" resource="http://rdf.freebase.com/ns/en/web_2_0" />
</body>

Faviki has implemented the Common Tag format (check out the extracted RDFa from Faviki Semantic Web topic page), and we hope that our users will benefit from it, as more publishers, developers and end users join in supporting the Common Tag format.

http://dbpedia/resource/Twitter
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Today our friends and partners at Zemanta launched a public semantic API, as well as a front side SDK.

Zemanta API analyses unstructured documents/texts and returns five types of content objects:

  • machine readable static tags
  • general categories and custom taxonomies
  • named entities with links to objects from major online knowledge databases: Wikipedia, Amazon, IMDB, RottenTomatoes, CrunchBase,… and to selected pool of online media and blogs
  • pictures from Flickr, CC sources and professional agencies
  • articles from selected media sources and blogs

Zemanta API analyses unstructured documents and returns five types of content objects

This is the first API that returns disambiguated entities linked to DBPedia, Freebase, MusicBrainz, and Semantic Crunchbase. The data can be returned in the standard format of Semantic web – RDF.

There is the extensive developers documentation available, including architecture overview, code samples for most popular programming languages, frontside integration SDK, developers forum and application gallery.

API is free to use for up to 10.000 API calls per month, and for a subscription fee above that.

Zemanta API adds great value to Faviki, by analyzing the text from web pages that are saved by users and suggesting related DBpedia concepts. This makes Faviki users’ lives much easier, because now they can add semantic tags with a just one click.

Zemanta API is a powerful technology that has lots of potential. We can’t recommend it highly enough. Keep up the good work Zemanta :)

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