How could Semantic Overflow eat its own dog food?

Semantic Overflow is great largely because it benefits from the good design and implementation of the StackExchange framework. Could our site be further improved with Semantic Web technology?

It's not just an academic question. Recently the community Q&A site Training Examples gained some visibility as a site

"Where data geeks ask and answer questions on machine learning, natural language processing, artificial intelligence, text analysis, information retrieval, search, data mining, statistical modeling, and data visualization!"

If you visit the site, you will see that it closely follows the Stack Overflow design, complete with tags, reputation, badges, etc. It uses QSQA, which is free software licensed under GPL and implemented in Python using Django. Site creator Joseph Turian mentioned a desire to improve it by applying machine learning and language processing techniques to its content.

So, how could Semantic Web technology be used to improve our own Q&A site?

There is currently a discussion within the Drupal community about how to implement a high quality StackOverflow type site using Drupal.

If this effort succeeds, the work would be a good base for a dogfoody version of SemanticOverflow, which could introduce the Linked Data aspects of Semantic Web tech to the site.

Consider using RDFa and terms from SIOC + CommonTags + MOAT (as per earlier comments by Tim Finn) for your Discussion oriented Linked Data Space. Use WebIDs that use FOAF+SSL (an enhancement of SSL2) for unambiguous and verifiable member identity, suggested earlier by Henry Story.

Simply providing an API also lays the foundations for others to accelerate this work. For instance OData, Atom, RSS feeds already enable me generate a moderate fidelity Linked Data Space from your data space by simply using our URIBurner service (which makes Linked Data Spaces from a plethora of data sources and associated resource types).