The Semantic Web is often described as an extension of the current Web. The idea of what extending the Web should look like can be seen in Linked Data.

In order to better understand the importance of Linked Data, one has to understand the context in which it emerged, i.e. the problem it has been trying to solve.

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Check out the new blog post Problems of the RDF syntax:

An RDF syntax (notation) is a concrete syntax for writing (serializing) RDF triples. There are many different notations, some of which are based on existing formats (XML, (X)HTML, JSON), while others use special formats (Turtle).

The problems of the RDF model, discussed in the previous posts, are inherited by RDF notations. Various special cases require special support in a syntax, which makes it more complex. However, RDF notations contain a number of their own problems, which can be divided into two categories:

  • various problems in RDF notations
  • the lack of a single dominant notation
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Check out the new blog post The Ultimate Problem of RDF and the Semantic Web on

In the previous posts I’ve covered two important problems of the RDF model, related to blank nodes and literals. Here, I’m going to focus on what I think is the key problem of RDF – the problem of the node of an RDF graph.

In the RDF: Concepts and Abstract Syntax, a node is defined as follows:

“The set of nodes of an RDF graph is the set of subjects and objects of triples in the graph.”

It then states that a node can be a URI reference, a literal or a blank node. However, the concept of a node itself is not clearly defined. What is it that is common to all different types of nodes? What requirements does a node need to meet in order to be a node?

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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 (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 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

I had a very interesting conversation with Jenny Zaino from 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.

Nova Spivack, the founder of Twine, held an interesting presentation about the future of the Web on the Next Web conference in Amsterdam. He thinks that we are currently in the process of Internet evolution in which tags are having an increasing significance. He predicts that in the next 10 to 15 years tags will have an increasingly important part while keywords will gradually disappear.

An interesting discussion about the subject took place on Techcrunch when Eric Schonfeld posted this thread asking the question “Is Keyword Search About To Hit Its Breaking Point?“. 97 comments have been posted so far and one of them especially caught my attention:

John Clarke Mills

Tags are nothing new, that is for sure. But what if you could tag an object, or entity, with another object. So instead of tagging objects with strings, which falls back on a simple full-text search, you could tag something with an actual representation?

I think that John has really nailed the point.

The problem with both keywords and tags is that they are just words. But what would happen if, instead of words, we used objects? What if we used unique concepts that would always and everywhere have the same name and would refer to the specific object?

Wikipedia & DBpedia

How can we reach an agreement on the names of such a large number of concepts? Well, it’s already been done and can be found in the largest collection of concepts in the world – Wikipedia. Wikipedia, besides having a standardized way of displaying articles, also has a standardized way of naming titles, which have been created and are constantly perfected by social consensus.

Currently there are over 2.36 million articles in English language on Wikipedia. The titles of Wikipedia articles are unique and cover almost all the concepts we can imagine.

However, the “problem” with Wikipedia is that it is not made for machines, but for humans. Its search capabilities are limited to full-text search, which only allows very limited access to this valuable knowledge-base.

Fortunately, there is DBpedia, which represents community effort to extract structured information from Wikipedia and to make this information available on the Web. The project uses the Resource Description Framework (RDF) as a flexible data model for representing extracted information and for publishing it on the Web.

For example, the web page about Semantic Web on Wikipedia looks like this, while on DBpedia it looks like this (there is also an alternative that is easier to read by humans).

This practically means that based on the name of the tag we can learn more useful information about that tag, its properties and connections to other tags. That is why I believe that DBpedia web pages are good representatives of the “objects”, the references of which will be tags.

Characteristics of new tags

Unique name

Unlike classic tags, which are just words, new tags represent references to unique concepts that have their own URL. For example, the tag “Coca-Cola” has a reference to URL (actually, the name of the tag is just the last part of the URL).

So, instead of having different tags for the same concept, which is the case with classic tags (cocacola, coca-cola, coca+cola, CocaCola) there will be just the one unique “Coca-Cola” tag.


But what if we wanted to add a tag that has more than one meaning? Let us look at the example of “library”. What are we referring to – “a collection of books”, “collection of subroutines used to develop software” or “the Seinfeld episode called ‘The Library'”?

It is simple – we’ll just use different tags: Library,
Library (computing)
and The Library (Seinfeld episode).

Tag properties and its connections to other tags

New tags are references to objects, and objects, as we know, have certain properties. In DBpedia there are some properties that are common to all tags, such as: an abstract, a picture (if existing), labels in multiple languages, type and subject to whom the tag belongs.

For example, if we look at DBpedia page for Keith Richards we can learn some additional properties about him (year of birth, type of voice, genre of music he plays…) as well as his connections to other tags (born in Dartford, current member of The Rolling Stones, plays Fender Telecaster and Gibson Les Paul, occupation: Music producer, Musician and Songwriter…).

Classification of tags

As I mentioned earlier, tags belong to different groups and form a structure. A system that supports such tags has an advantage over other systems because it automatically classifies tags and so “knows” what Microformat, RDFa, Web Ontology Language and Thesauris have in common. They all belong to the subject Knowledge representation. That’s why with Faviki it is possible to follow the content by subject and not only using one tag (see Knowledge representation page).


I think that tags will truly dominate in the near future. But those will not be the tags that we are used to, but their “smarter” offspring. I believe that the results of this evolution will make the foundation for the future Internet which will handle objects and their properties instead of just web pages. Present situation is not ideal but it makes a good foundation for the development of the universal language that could connect people and the Internet in new and exciting ways.