Towards a Smooth Semantic Web

This is an almost complete draft I wrote almost 2 years ago. I thought I should publish it because it is basically complete, and because my next post will be about the new language and technologies that will make the "smooth semantic web"  a reality. And for the record, 'smooth semantic web' was a provisional name to be changed before publishing.
Here it is:

The Semantic Web is slowly building up and will eventually grow to a critical mass where it will become useful. How useful is another question. Will it revolutionize the web? Web 3.0? Maybe. Maybe not. But it will definitely have a use and an effect. The current standards such as RDF, OWL, SWRL, SPARQL, and SKOS are good, and still have a ton of potential that is growing exponentially as we speak. But these standards are not enough. OWL and SKOS can only capture so much knowledge. They can capture crystalized categorizations of well defined concepts. But much, if not the vast majority, of knowledge is not found in strict categorization and necessary relationships. So what is missing?

People have made attempts to extend OWL in a few different ways to make it cover a wider range of statable knowledge. People have looked into adding probability, non-monotonicty (time), belief. I propose a different addition which, if adopted, would add a prior layer to OWL, just as the above attempts do.

Consider a music ontology. OWL can support certain relationships such as: Rock ISA Genre, Track isPublishedOn CD, etc. But the certain pieces of knowledge cannot be represented because they haven't been crystallized to the point of being definable, especially when you consider the categorization of instances. Imagine the daunting task of deciding whether certain border line songs count as belonging to a specific genre. But consider the task (still daunting, but not as much) of deciding whether a song is "better classified as Rock than Rap" for instance. It may be difficult to say that a song is Rock, or is Rap, but it may be clear which one it is closer to. Much more can be said.

Notice that we are not talking about probabilities here. It isn't semantically correct to say that a song is "more likely" categorized as Rock than Rap. We are saying that if we were to categorize it as either Rock or Rap, it would be better categorized as Rock than Rap.

You may be wondering what the criterion for "better" is or should be. This itself should be represented within a knowledgebase.

So how would we use this knowledge? SWRL and SPARQL can only handle deductive reasoning, so they won't help. There are two options, the way I see it. Take my smooth knowledge and use some kind of classifying step to derive a regular rigid knowledgebase. The other option is to invent smooth reasoners. I think vector space representations and abduction would be important for this step.