New blog posts on Faviki

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

Faviki is featured on Google Code

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.

Faviki uses Zemanta API to suggest semantic tags

We’re excited to announce that Faviki has officialy started to use Zemanta API to suggest possible Wikipedia concepts.

Zemanta is a platform for accelerating on-line content production, by recognizing contextual content and instantly serving relevant images, smart links, keywords and text to the user. Recently they launched an early release of the API and allowed web developers to use Zemanta engine in their applications. The API allows developers to use simple RESTful interface to get suggested images, articles, tags and links in a structured format (XML, JSON, ..) for a given piece of text.

The fact that it suggests Wikipedia links, among others, was particularly interesting to me, so I tested it right away and figured out that it works very well. Now Faviki users don’t have to start from the stretch, because tag suggestions are given through an analysis of the web pages’ content. If a suggestion is OK user just needs to click on the ‘+’ button and the tag is added.

However, do not expect it to do the entire job for you – you are the one who makes the final decision!

We also started using Zemanta on our blog (it can be deployed on all major content publishing platforms). It’s easy to use and it saves time, so we highly recommend it. Great job, Zemanta!

Many thanks to Andraž Tori for the support.

Related articles by Zemanta

• Zemanta API early preview release
• TechCrunch Launches CrunchBase API
• Get Started With REST