For a couple of weeks I’ve been twitting (and tweeting) more than usual, because
- I’m helping in both Transket‘s and Natouring‘s communication campaigns
- I’m trying to understand better when (and how) to use Facebook, FriendFeed, Delicious, Yammer or Twitter (I also blog and wiki, but these I know when and how).
- I’m introducing my students into Twitter, after having introduced them into CiteULike, Delicious; before we go into FriendFeed and GoogleReader. (I’m sorry I’ve given up Twine and Gnoss for the moment; interesting but not fluent).
So I’ve also been testing several Twitter related services, in particular
- FileSocial, a very effective and safe way for sharing files (I’ve shared a MP3) of up to 50MB.
- Seesmic, the best web tool I found to monitor users, lists of users and hashtags.
- TwitR, a directory of users classified according to their favourite hashtags. Very convenient if you are looking to expand your contacts and lists with users with whom you share interests.
(Barb Dybwad, @Mashable, suggests 12 more interesting things to do with Twitter).
Linked data value spiral (Bnode.org)
Ok, then, I’ve been converted, I now understand the power of twitting/tweeting, following, listing and being followed and listed. The most appealing aspect of it has been to realize that every element in Twitter is a potential linked data:
- Name – Location – Web – Bio
- Following – Followed – Listed
- Favorites – Lists
- Tweets (full of linkable data as URLs, hashtags, usernames, named entities, keywords and other content terms)
Twittshpere. All these elements are structured and neatly related. The amount of informatin is huge (3 million messages a day, more than 1 million users in March 2008). It conforms an open, rapidly expanding and meaningful sphere of data, i.e. it is fresh food for linked data.
Linked Data. Tim Berners Lee et alii define linked data as a method of exposing, sharing, and connecting data via dereferenceable URIs on the Web. I’m particularly fond of open datasets.
Linking Open Data (source Wikipedia): A project to extend the Web with a data commons by publishing various open datasets as RDF on the Web and by setting RDF links between data items from different data sources. In October 2007, datasets consisted of over two billion RDF triples, which were interlinked by over two million RDF links. By May 2009 this had grown to 4.2 billion RDF triples, interlinked by around 142 million RDF links.
- DBpedia – a dataset containing extracted data from Wikipedia; it contains about 2.18 million concepts described by 218 million triples, including abstracts in 11 different languages (see the very DBpedia resource associated to the present wikipedia page)
- DBLP Bibliography – provides bibliographic information about scientific papers; it contains about 800,000 articles, 400,000 authors, and approx. 15 million triples
- GeoNames provides RDF descriptions of more than 6,500,000 geographical features worldwide.
- Revyu – a Review service consumes and publishes Linked Data, primarily from DBpedia.
- riese – serving statistical data about 500 million Europeans (the first linked dataset deployed with XHTML+RDFa)
- UMBEL – a lightweight reference structure of 20,000 subject concept classes and their relationships derived from OpenCyc, which can act as binding classes to external data; also has links to 1.5 million named entities from DBpedia and YAGO
- and more…
Who will be first adding to this list open data from the Twittshpere? You bet!
References
- Benjamin Nowack (2009-02-18). The Linked Data Value Spiral, in Bnode. Retrieved November 8, 2009, from http://bnode.org/blog/2009/02/18/linked-data-value-spiral
- Linked Data. (2009, October 30). In Wikipedia, The Free Encyclopedia. Retrieved 08:39, November 8, 2009, from http://en.wikipedia.org/w/index.php?title=Linked_Data&oldid=323006663