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Too far. It did work last time. AI systems, notably expert systems written in languages suited to the task such as Prolog or LISP work. SGML works too. (Corrupt history leads to corrupt conclusions, and that is the SemWebs biggest and most intractable obstacle.) What was learned is that AI is tedious to write and difficult to scale. Say: expensive. Following these threads, the point of the SemWeb seems to be: 1. One language moreorless, so as cheap as possible. 2. One language moreorless, so scaling comes of linking and that is what networks do. 3. Applications of the linked information: TBD. I agree that the frustration of STimBL down to Elliotte is in item three. Systems doing this work do it without items one and two so items of type three never get on the radar. This may come down to 'not enough customers really want machines to do this work' for reasons which are not coupled to the technology. See para 1 above. The 'telephone to financial conversations' app is called a 'link analysis' application. Those are used in the industry I work in. Abstractly, mining hidden links among loosely coupled processes can be applied to other information domains. How to treat these as so-called 'proofs' is interesting because the measure of 'proof' always takes in more 'proof systems' and rules. The measures of proof for a technician repairing an engine and that of a judge deciding if a warrant is merited are not the same. len From: Miles Sabin [mailto:miles@m...] For seconds, I think it's quite reasonable to question whether it's worth even bothering to read TFM if it's more or less the same old stuff that didn't work last time with no reasonable expectation that the new twist (angle brackets and URIs) is going to help find a useful route out of the blind alley.
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