So how does the semantic Web partition work again?

An Ontology chart from Wiki

Let's use the blog as a platform. With  the blog  semi-structure, a semantic reduction can be performed on the unquoted text in each blog post.  This would be the XML based, graph of possible key words in an ordering, taken from the authors original content. This semantic definition is matched against other semantic definitions already obtained, and inserted into the we larger semantic graph, possibly pruned.

So the blogger platform with its semi-structure might just make an ideal middle ground for semantic learning on the web.  From an open source perspective, the next step is to search out other with appropriate pieces of software, and maybe some predefined standards.  Then I will link to this prior at in a way that describes the new web model.  Others with competitive models will respond, and the idea will move forward, part cooperation, part competition.

(PS, this is a bit of old hat, I looked very carefully into this exact concept, in depth in some prior work)

Another Wiki:
Latent Semantic Anaysis

Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur close together in text.

Its a patent claim! I will check into it.
Here is one from Wiki without the patent, and more closely resembles the idea I propose:

Semantic matching is a technique used in Computer Science to identify information which is semantically related.
Given any two graph-like structures, e.g. classifications, database or XML schemas and ontologies, matching is an operator which identifies those nodes in the two structures which semantically correspond to one another. For example, applied to file systems it can identify that a folder labeled “car” is semantically equivalent to another folder “automobile” because they are synonyms in English. This information can be taken from a linguistic resource like WordNet.