RDF Semantic web research isn't working
zacker.org
zacker.org
Once you start to sit down and try and encode all knowledge in a formal manner, one begins running into Godelian imcompleteness. Tugging on the string and trying to resolve inconsistencies leads to more special cases and more descriptions, which in turn lead to more special cases and more descriptions, and so on. In "Ambient Findability", a book on the future of information finding, author Peter Morville calls Knowledge Representation the "quagmire" of good old-fashioned AI.
There is a certain benefit to the formal representations used by the semantic web. Namely, that communication between different systems is enabled by communicating using discrete tokens. The discreteness of these tokens make the choice of token unambiguous, but does not mean that it is a perfect map of meaning. This is similar to language, which is an incomplete and imperfect representation of what is nebuluous and informal in the brain. For example, if I use the word "healthy", you can be pretty confident that I used this particular word (assuming you heard me okay), but you are not necessarily confident that you know precisely what that actually means. We communicate with distinct tokens as our language not because it is a perfect map of meaning, but because its better than using continuous linguistic representations. Imagine if you weren't even sure what word I used at any given point in time, because words are fluidly flowed into each other. So even though knowledge is better modeled probabilistically and continuously, it might be difficult to communicate with a language whose representations was explicitly probabilistic and continuous.
The semantic web shouldn't be viewed as a be-all end-all perfect encoding of knowledge. Rather, it should be viewed as a language for machines to communicate, in an imperfect way, but better than having no language at all.
There are good open source tools (Sesame, Jena, Swi-Prolog semweb libraries, etc.) and commercial support (AllegroGraph, etc.) If the motivation is there, the tools are available.
Also, Reuters' Open Calais (free) web services do a very good job at generating semantic data automatically.
The next thing we need is simply more people writing applications that use linked data sources.
A tired cliche, but: this is like FAX machines, eventually there will be more available linked data so the rate of app development will increase, and vice-versa.
What semantic research regularly ignores is that describing the system with a semantic representation, is roughly equivalent to the effort and scale to make the original. Until a semantic graph describes the universe it is expected to reason about (which is akin to being the universe), its utility is extremely limited. Even domain specific applications might contain billions of entities and relationships. Who builds this giant hairball? The argument is that the crowd should, but I'm too busy building my application to build what essentially amounts to a bunch of virtual synapses in a virtual brain that I'll derive no benefit from in the lifetime of my web app.
On top of that, we know almost nothing about building the reasoning systems on top of this kind of enormous graph. We have little toy models of a few billion relationships, but what we want from the semantic web is a virtual agent with super-human intelligence capable of reasoning about the world like we do, just using a brain with the capacity of the internet. Basically, we want Kurzweil's singularity. But we have no real idea beyond very simple cases where the reasoning systems on these toy model do anything generally useful. The reason our brains work is that we are designed to work on limited sets of data, and infer information that we don't know about. We popularly call this imagination. For an idea like the semantic web to be useful before the graph is complete, the reasoning systems have to essentially be able to imagine. Data completeness and logical inferencing on that graph are simply not enough.
Even experiments like Cyc, where many man decades have been spent building essentially a similar thing, have yielded no real applications...even on very constrained and extremely limited domains like the Terror Knowledge Base. You know why? Because reasoning about the motivations of a terror group quickly start to involve secondary and tertiary considerations (and higher) to be useful. Two bad guys working in coordination might not make any sense unless I know that they went to middle school together. No I have to teach the system what a school is, what classes are, why that makes a difference, how that might impact future predictions....and now I'm quickly down in the weeds. And the system is not likely to be able to imagine different kinds of schools and then different kinds of social gatherings etc. that might predict similar kinds of coordination in the future.
The problem is not a chicken and egg problem exactly (nobody will participate in constructing the semantic web until the semantic web is up and running). The problem is even if all of us started tomorrow to build the semantic web. It would be generations before we had a real graph to work off of. Who knows how long before we had enough reasoning systems on the graph to do anything really useful. And for all that effort? Perhaps a few percentage points difference over the capabilities we already have with far less effort. Until we can take something like the Cyc project, and turn it into a useful system right now, larger scale systems like the Semantic Web are essentially useless.
The real problem though is that semantic we researchers simply aren't learning from these prior efforts. Their idea is to just "do it bigger". Which is not helpful. The semantic web is supposed to be a model on the world. But our small models of this proposed model don't actually do anything. Even a toy car can still roll. The real answer is, let's figure out how those efforts failed beyond just not being "big enough". Basically we failed in the past, and the idea is to just try harder -- this is essentially the definition of insanity.