Thanks for the education keefe. Maybe what I'm expecting isn't a fully directed graph but a set of automated tools that can help associate disparate data sets. Do you have any good suggested reading you could point me to on the semantic web?
The basic information is pretty well summarized here http://www.rdfabout.com/ Automatically mapping disparate data sets is a pretty hard problem that does not tend to automate well. My current company produces tools for this and you can download a beta here http://www.topquadrant.com/products/TB_Composer.html The basic technique is to import your datasets (XML files or RDB or whatever) into RDF, then define a master data model in RDF and define a set of sparql construct rules (or SPIN rules, spin is one of TQ's proprietary languages) which says what I see data with structure X in data source A construct structure Y in data source A using the fields of X. The chief scientist at TQ has written a book on how to effectively do modeling with RDF : http://workingontologist.org/ There are a number of other books and tools, you can checkout the semantic technologies conference for exhibitors for a full list.
Thanks I'll check it out.
I've been using a simple approach of allowing folks like Zemanta to extract meta tags from content (natural language text), weighting them by confidence levels or reuse and then finding similar combinations/weights. Link by content.
I'm not sure exactly what you mean, are you talking about entity extraction with confidence levels ala Calais? That used to be clearforest before Reuters bought them. I think entity extraction in general is so closely related to natural language quantification that it is one of those generally tough problems. In general, I feel work in this area suffers scalability issues and highly optimized point solutions will do better for a while yet. I guess we'll have to see how Bing ne Powerset does in mainstreaming this whole thing or if it'll be just a bunch of hype like twine.
I think sense disambiguation is certainly one of the most interesting applications of semantic technologies. Here at TQ, the focus tends to be on the exploitation of referential semantics and structured data to establish master data models, merge data models and provide a platform for model driven application development and analytics. However, the focus here tends to be more towards corporate IT than mainstream web dev.
