W3C Semantic Web Activity logoWe 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.

Reblog this post [with Zemanta]

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 🙂

Reblog this post [with Zemanta]