Linked Data Resources

A selection of my favourite resources (videos, tutorials, blogs, books, conferences, reports and quotes) introducing and describing the Linked Data idea. But, first I would like to highlight the 4 principles of Linked Data, and also the nice idea of a 5 star rating scheme for Linked Open Data.

The 4 principles of Linked Data  
The 5 stars of Linked Open Data
Videos and Audios
Tutorials
Blogs and Wikis
Presentations
Books
Workshops
White papers and Reports

Quotes

Data integration is done on the application level,
linked data does it on the data level.


While big data will make an organisation smarter
and more productive, open data and linked data
will make it richer.

The role of information in the modern enterprise architecture programme,
Computerworld UK, reporting  results from Gartner's recent global
enterprise architecture (EA) survey.


Linked Data techniques allow organizations to publish more,
reuse more and combine more data
for a fraction of the cost of older methods

3 Round Stones CEO Bernadette Hyland
Taking Inspiration from Open Government Initiatives


We believe that Linked Data has the potential to solve some important problems
that have frustrated the IT industry for many years, or at least to make significant
advances in that direction.

IBM developersWorks
Toward a Basic Profile for Linked Data


Linked Data ... a global information graph that users and applications
can seamlessly browse by resolving trails of URI links ("following one's nose"),
a data powered form of "toURIsm."
Library Linked Data Incubator Group Final Report


In addition to making flatfiles available to download on the Web, and providing applications that enable programmatic access to backend databases through the Web, imagine using the Web itself as a database: a massively distributed, decentralized database. This is what Linked Data is about – putting data in the Web.



Linked Data could provide the antidote to the chaos
and complexity of the current overabundant array of
too simple search mechanisms with too little precision and
too short recall of relevant results.