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

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

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!

As 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

We are honored to have been invited to write a case study about Faviki and the idea behind semantic tags for the W3C Semantic Web Activity website.

The goal of W3C SW case studies is, primarily, to help the Web community at large understand and appreciate the advantages of possibly using Semantic Web technologies in real applications. It was a challenge to write a document that should convince (often skeptical) IT  managers and other technology people that there can be made some interesting applications based on SW technology.

I tried to show the benefits of  using the semantic tags and described how they are used in Faviki. The key idea of the case study is that the semantic tags, as an intersection point of Web 2.0 and the Semantic Web, have the potential to enable much faster evolution of the Web by providing a solid foundation from which the Semantic Web can grow soundly.

I already wrote on this blog about the need for a tag evolution back in May, so I was happy to present the idea, that has matured in the meantime, to a wider audience.

Many thanks to Ivan Herman for this opportunity and the comments which helped make the entry better.

Also, a big thank you to Maja, Sebastian and Rod for their suggestions.

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

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

Related articles by Zemanta

• Zemanta Launches Public API To Fuel Content Suggestion Engine Adoption
• Zemanta Open Semantic API with commercial support
• Zemanta and the Linked Data Cloud…
• Faviki is featured on Google Code

ReadWriteWeb, a popular blog about web technology, has started publishing its annual list of “10 Semantic Web Apps to Watch” last year. This year, I’m happy to announce that Faviki made it to that list.

As the number of Semantic Web startups rapidly increases, I understand that editors at RWW consider this list to be a prediction of success in this brand new part of the market. I am very pleased that Faviki’s idea of semantic bookmarking quickly caught their eye.

I suggest you check out this list. You will find some very interesting and diverse projects, ranging from semantic search engines to resaurant review web sites.

Related articles by Zemanta

• 10 Semantic Apps to Watch – One Year Later
• Web 3.0 & the Power of the Semantic Web
• The Map of Data: Over 10 Billion Pieces of Reusable Information

Good news everybody! Faviki got into second round of Mashable Open Web Awards. Thanks everyone who nominated it! If you want to help us by voting, you can do it here.

Mashable has announced their 2nd Annual Open Web Awards. It is the international online voting competition that covers major innovations in web technology.

Nominations of sites/companies are made by community in 26 different categories. The category we’re competing in is social bookmarking.

You can vote for Faviki here by entering your e-mail address, and confirming it in the mail you’ll receive after voting.

You can also vote for our partner Zemanta here (blog plugins category).

Note that you may nominate a site/company in as many categories as you see fit. However, there is only one nomination per category per e-mail address.

Big thanks for your support!

Related articles by Zemanta

• 2nd Annual Open Web Awards : Nominate Your Favorites
• mashable’s 2nd annual open web awards
• Help us By Nominating Zemanta in Mashable Open Web Awards

Faviki has been covered on Mashable in the Startup Review category. The post is titled “Faviki Brings Wikipedia and User Notes to Social Bookmarking”. Here is what Paul Glazowski had to say about Faviki:

If you’re willing to try something different, or have never fallen under the Delicious spell, Faviki is quite good. Simple and powerful are two elements it exhibits, and the smart design can grow on the user rather quickly. Altogether, Faviki is impressive. For a social bookmarking service, that’s certainly saying a lot.

Read the full post here. Thank you for the kind words, Mashable!

Image by Thomas Hawk via Flickr

Faviki is a featured project on Google Code for it’s creative usage of Google AJAX Language API!

This API allows you to translate and detect the language of blocks of text. Despite the fact it has a word “AJAX” in it’s name, the API can be also accessed from non-JavaScript environments.

What is it all about? As we have written recently, Faviki uses Zemanta API to make auto suggestions for tags. That’s OK for English pages, but what about other languages?

They have to be translated first, so Faviki asks Google AJAX Language API for help A great thing is that you don’t need to specify the original language, it recognizes it automatically!

Automatic translations made this way are not perfect, but they seem to be good enough for Zemanta to find appropriate concepts from English Wikipedia, which are finally translated again into user language (using DBpedia data about language connections).

So, the whole process looks like this (simplified version):

1. Faviki fetches a web page and extracts a core text (without HTML and non-relevant content).
2. Then it tries to figure out if a content is in English. If it isn’t, it is sent to Google language API, which detects the original language automatically, translates it into English and returns the translation.
3. The content is then sent to and analyzed by Zemanta API, which then finds relevant links. Faviki uses links from English Wikipedia – titles are used as semantic tags.
4. If users language is not English, we must translate them. Using DBpedia datasets “Links to Wikipedia Article” , we can find names of  Wikipedia’s  titles in one of 13 languages. These datasets actually contain the connections between English Wikipedia articles and articles from Wikipedia in other languages.
5. Finally, suggested tags are offered to a user.

Faviki combines three services to make multilingual semantic tags possible. We hope this will help our non English speaking users to tag their bookmarks faster and more easily. These great services will continue improving in time, so expect that the suggested tags will be better, too.