Take bookmarks for example

The client has a list of them, and he clicks on them once in a while. When he does, a tiny search graph is launched to collect the key word a node up and a node down, at the destination URL. It is a graph that collects the ontology of another graph and returns that as a graph, back to the client. Where to put it? In the client bookmark triple store. So run a utility store that rewrites the bookmark store in nested order including the newly acquired ontology, where not matched. Or use table links to keep scrap book information linked to URLs. Or grab chunks of text and add it to a utility column on your bookmarks, a general test blob per mark. Later accessible by the column selection property with an imposed nested schema.

News sites, wiki sites, corporate sites would produce ready made ontologies, clients widgets grab them. Next time through the client gets a quick pop up of recent possibilities, from the clients local store. Clients set slider bars and do clicks thrus to keep the ontologies to maximum entropy encoding. If the client ever grabs text from a return site, just link it in as a terminating text blob in some local ontology, in the client database. Quick, decisive personal information processing of the web, as seen thru graph traversal.

Clickable, slideable, dragging rapidly through graphs visually backed up by high speed local sqlite3. Tagging nodes for deselection, moving nodes up or down, making new node sets. Right on our browsers and local db. Take a search graph, your favorite, right there inn your browser and drag it into one or two new web sites. See if the new sites know what your search graph knows.

Another example, search graph normalizers. Wiki might give out graph traversals that expand and normalize a random word search for the user. Google does this, its a snap with graph traversal. Serious searchers and writers download keyword lists, make them available for specialized search expansion or text matching and deselections. The blog industry would boom, specialized blogs naturally developing shared ontology graphs. Writing code a breeze as the natural caching of the system is almost always on time to grab a software definition, even as you switch languages.