Links:
1. http://virtuoso.openlinksw.com/images/dbpedia-l...
2. http://esw.w3.org/topic/SweoIG/TaskForces/Commu...
3. http://dbpedia.org/resource/Linked_Data – cross linked with Freebase and many other structured data spaces

As it relates to tagging words, isn't this just XML? And don't we also need to tag whole phrases and not just words?

For example, in a document that it tagged as about MSFT, references to Gates are statistically more likely to refer to the person rather than the object. So, instead of tagging (codifying) each individual reference to Gates in the document, context can be inferred from one single tag, and hence the ambiguity resolved.

I am not sure it makes any difference at all WHAT the thing is, it's more about the interrelatedness of one word to other words. In that case, the ambiguity is represented in a set of linkages that are more or less exclusive.

For example – the linkages to gates the thing vs gates the person would be different. Even in the case of that double entendre, the two sets could be statistically separable.

Terry's argument regarding the objective definition of meaning refers to the term's traditional philosophical usage, whereas Gerry and direwolf are talking about contextual ambiguity.

I believe pursuing semantics (philosophical) in computing is a futile endeavor until machines are able to feel the wind on the their faces.

Resolving contextual ambiguity, however, is a much more attainable and in many ways more useful goal. How many times have you Googled something only to be returned hundreds pages with the 'other' use of your key word?

Whether progress is made via changes in representation, better algorithms or even some sort of stochastic analysis is largely irrelevant (to me).

The key point is that whoever makes progress in this space will, as the VCs like to say, take away a lot of pain.

I wasn't being very nuanced in my original comments. That's partly due to lack of time, partly due to liking a more colorful debate. So here are a few more thoughts, and some pointers.

Consider Artificial Intelligence and its pursuit of intelligence. We once thought it took real intelligence to play chess (for example). But as we got better and better at engineering, and we thought up smarter (but completely mechanical and non-mysterious) algorithms, we moved the goalposts. I.e., we decided that actually you didn't need to be “intelligent” to play chess after all.

I don't believe that “intelligence” corresponds to any “thing” either, just like I think “meaning” and “understanding” are also just words. What I do believe however is in engineering and tool-building. We're primates, and primates are pretty good tool builders. So I often suggest to people that they spend less time (and investment monies ) on chasing abstract words and more time on building tools.

The lesson of AI seems clear. If your tools are good enough, you can give the illusion of intelligence up to and beyond (i.e., beyond grandmaster) where it matters in any practical sense. The computer plays chess so well that you might as well say it's intelligent, or not – it just doesn't matter anymore.

And I believe the same is true of semantics, and going after meaning and understanding. Those things can perfectly well not really exist while at the same time we can practically achieve them (i.e., the convenient and practical illusion, as with intelligence for the purposes of chess playing) by just focusing on engineering and tools.

Make sense?

From that POV, I argue that huge strides can be made by improving representation. If you get representation right, things that look like problems can simply go away. If you get the representation right, you may not even need a clever algorithm. Can you do an end-run around Google's armies of PhDs by changing representation? I.e., don't challenge them on the algorithm front, where you're bound to lose, but change the ground under them. You wont be surprised to hear that I think the answer is yes. I'm not talking about “beating” Google as a company, but of taking search – and how we work with information in general – to a new level.

I wrote about this at some length, back before it was so fashionable to be me

The main posting is http://www.fluidinfo.com/terry/2007/03/19/why-d...

And there are several others, including some that give very simple examples of why representation is so important, at http://www.fluidinfo.com/terry/category/represe...

In summary, I don't think the words matter much. I think we can achieve amazing results (things that look like real intelligence, real understanding, that somehow capture meaning, etc) simply by focusing on engineering. My best bet about where to focus is on representation. What are the implications of the various new ways of representing information that we're exploring? I've been pondering that for over a decade! My own bet, via Fluidinfo, definitely has some strong advantages and some strong weaknesses. It's a tradeoff, like so many things in computer science. Other approaches represent different tradeoffs. It's far from clear what will “win”. But as I said in my earlier comment, it's a vast space we're starting to explore, and, I like to imagine, there's plenty of value to go around.

I hope that's a clearer and a more useful answer.