Posted by John Klossner on Aug 03, 2009

For those of you, like me, who need a way to keep these things straight, I offer the following handy, wallet-sized program.

WEB 1.0 (browsers) – Users find data
WEB 2.0 (social networks) – Users find each other
WEB 3.0 (semantic Web) – Data find each other

Of course, a lifetime of science-fiction reading and viewing leads me to fear we can look forward to the following developments:

WEB 4.0 – Data create their own Facebook page, restrict friends.
WEB 5.0 – Data decide they can work without humans, create their own language.
WEB 6.0 –Human users realize that they no longer can find data unless invited by data.
WEB 7.0 – Data get cheaper cell phone rates.
WEB 8.0 – Data horde all the good YouTube videos, leaving human users with access to bad ’80′s music videos only.
WEB 9.0 – Data create and maintain own blogs, are more popular than human blogs.
WEB 10.0 – All episodes of Battlestar Gallactica will now be shown from the Cylons’ point of view.

ABSTRACT
One of the core goals of the Semantic Web is to store data in distributed locations, and use ontologies and reasoning to aggregate it. Social networking is a large movement on the web, and social networking data using the Friend of a Friend (FOAF) vocabulary makes up a significant portion of all data on the Semantic Web. Many traditional webbased social networks share their members’ information in FOAF format. While this is by far the largest source of FOAF online, there is no information about whether the social network models from each network overlap to create a larger unified social network model, or whether they are simply isolated components. In this paper, we present a study of the intersection of FOAF data found in many online social networks. Using the semantics of the FOAF ontology and applying Semantic Web reasoning techniques, we show that a significant percentage of profiles can be merged from
multiple networks. We present results on how this affects network structure and what it says about relationships and individual behavior. Finally, we discuss the implications this has for using web-based social networking data to create intelligent user interfaces and social software.