Twitter has emerged as a very powerful medium for propagating ideas and thoughts. Possibly Twitter is the ideal data input tool for harnessing the collective insights of the humans and systems that are connected to the web – effectively a significant proportion of all humans and virtually every non-trivial system on the planet.
By simply adopting a convention of twittering important insights in the format <some URL> <some relationship> <some other URL>, users can incrementally, one step at a time, create a personal model of the web. These personal models can grow arbitrarily large, and Twitter is certainly not the appropriate tool for visualising, modularising and analysing such models. But arguably, Twitter is the most elegant and simplest possible front end for capturing atoms of knowledge.
Note that URLs used on Twitter typically point to a substantial piece of information, and not a simple word or sentence. Often a URL references an entire article, a web site, or a non-trivial web-based system. These articles, web sites or systems can be considered semantic identities in that specific users (or groups of users) associate them with specific semantics (or “meaning”). Hence tweets in the <some URL> <some relationship> <some other URL> format suggested above represent connections between two semantic identities. A set of such tweets amounts to the construction of a mathematical graph, where the URLs are the vertices, and the relationships are the edges.
If we add functions for transforming graphs into the mix, and considering that we are connecting representations of semantic identities, we end up in the mathematical discipline of model theory. Considering further that Twitter models are user specific, and that the semantics that users associate with a URL are not necessarily identical – but rather complementary, we can further exploit results from the mathematics of denotational semantics. For the average user there is no need to worry about the formal mathematics, and it is sufficient to understand that the <some URL> <some relationship> <some other URL> format (I will use #URLrelURL on Twitter when referencing this format) allows the articulation of insights that correspond to the atoms of knowledge that humans store in their brains.
With appropriate software technology it is extremely easy to translate sets of #URLrelURL tweets into a proper mathematical graph, and into a user specific semantic model. These models can then be analysed, modularised, visualised, compared, and transformed with the help of machine & human intelligence. Amongst other things, retweets can be taken as an indication of some degree of shared understanding in relation to a particular insight. Further qualification of the semantic significance of specific tweets can be calculated from the connections between Twitter users, and from analysis of the information/functionality offered by the two connected URLs.
The most interesting results are unlikely to be the individual mental models that are recorded via #URLrelURL tweets, but will rather be the overlay of all the mental models, leading to a complex graph with weighted edges, which can be analysed from various perspectives. This graph represents a much better organisation of semantic knowledge than the organisation of information delivered by systems like Google search.
Instead of processing semantic models, Google search must process entire web sites with arbitrary syntactic content, with no indication of which pairs of URLs constitute insights useful to humans. Google can only indirectly infer (and make assumptions about) the semantics that humans associate with URLs by applying statistics and proprietary algorithms to syntactic information.
In contrast, the raw aggregated #URLrelURL tweet model of the world captures collective human semantics, and any additional machine generated #URLrelURL insights can be marked as such. The latter insights will not necessarily be of less value, but it will be reassuring to know that they are firmly grounded in the collective semantic perspective of human web users.
Making this semantic perspective accessible to humans and to software via appropriate search, visualisation, and analysis tools will constitute a huge step forwards in terms of learning, effective collaboration, quality of decision making, and in terms of eliminating the boundary between biological and computer software intelligence.
Therefore, please join me in capturing valuable nuggets of insight in the format of
<some URL> <some relationship> <some other URL> tweets.
http://gmodel.org #gmodel can be used to #translate twitter models into #semantic #models http://bit.ly/em60Tw