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Here is a post that didn't stick last time for some reason, which Mike Champion responded to here - http://lists.xml.org/archives/xml-dev/200304/msg00707.html Thinking about Emergence and adaptive behavior and how it relates to semantic versus AI -- Steven Johnson has some interesting ideas about meaning in networked communities. http://www.amazon.co.uk/exec/obidos/ASIN/0140287752/qid=1051183047/sr=2- 1/ref=sr_2_3_1/202-4384120-8985419 While not directly referring to the semantic web, if I remember right, he has a pretty neat theory about the role of computers when it comes to an emerging connected intelligence. It is of course humans that provide semantics and understanding, not computers. On the other hand, computers can, and already do, play an intriguing role in helping us to identify what is of importance and what is not. That is, through monitoring our behavior. Monitoring the behavior of individuals that use the web, to deliver scores and so on, based on the importance of that information. It is only by interfacing with the resource that meaning is generated---if computers are tracking our interactions, then statistically they can usefully help to analyze what has "meaning" and what does not. This potentially creates meaning when these systems include feedback loops. Thus complex semantic behaviour emerges from simple rules, while IT plays the role of keeping score; and feeding back. Slashdot is one example Johnson uses. as he points out however, at the moment Slashdot is scored according to the tyranny of the masses---that is, the folks that the majority agree with end up being moderators. However Johnson posits a simple rule change which would add more diversity to the meaning of the dialogues within the Slashdot system--that is, by choosing moderators based on the diversity of opinion they generate. "dissenters" must have a voice--because quite often that voice is saying something useful in the wilderness. That is not to say "whackos" should have a voice---scoring based on quality is still crucial to the overall system. The folks that make quality responses that also arouse passions in the listener are likely to generate the most "quality" in terms of ideas and feedback. Suffice to say that meaning can emerge from behavior. Let's look at Amazon rankings and recommendations for example. Does Amazon know what are good books or records or whatever. Absolutely not. But WE do. It is by monitoring the behavior of large numbers of people that we get a sense for what is good or not. What I am trying to say - is that we perhaps need a more sophisticated understanding of the role of computers and networks in helping us with decisions of meaning. Johnson provides some interesting potential pointers in that direction. In a way--sorry to get on the hobbyhorse again folks-- this brings us back to Wittgenstein's theory of "meaning as use" - the meaning of a word or concept is related to its use by communities. Rather than to any essential "essence". What is perhaps extraordinary and fundamentally different about the web and connected networks is that now we begin to have a mechanism for understanding and auditing how these concepts are "used" by different sets of communities. That is we can see "meaning as use" in action--but seeing who is listened to, what data sources are "trusted". I hope the above rant doesn't seem too tangential, but I really wanted to at least point to an alternative conception of "meaning" and how it related to computers. Computers are not good at making subjective judgments and or semantic distinctions, but they are (the web is) perhaps the only way to track the subjective judgments of vast numbers of people---from this analysis meaning and semantics can arise. I am not sure that AI or semantic web approaches are the only ones--for one thing, as as someone else has pointed out--relying on folks to do decent semantic markup is most unlikely, if not possible. I mean--how many of you do a decent job documenting the code you write? However - the net can be used to identify what IS well marked up--by seeing who, and how many, use it and ranks it highly--and in the end that could be said to be what has "meaning". Then there is the potential to introduce new rules based on this--wait for it folks--metadata. That is right, it turns out the computers in this scenario generate useful metadata---data about data. But that is based on how, when, why the data itself is used, rather than by identifying the "meaning" of specific elements. The relationships between resources, elements and people, are where we find meaning. Computers are very useful tools for monitoring these interactions. Obviously this is a form of statistical analysis, but I figured it was relevant and differentiated enough to be worth mentioning in this context.
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