NOTE: In case you missed it, here is a link to a screencast of Kenny Bastani's webinar about using the Neo4j graph database in text classification and related Deep Learning applications. It's a fascinating introduction to some original work Kenny is doing that leverages the strengths of a property graph, in this case Neo4j, to do some Deep Learning text-mining and document classification.

Karma is an amazing Open Source "multilingual" ontology-aware cross-model smart-mapper providing "Rosetta Stone"-like powers to users coping with the ever-shifting publication of Linked Open Data (LOD). Karma is the evolving brilliant work from the incredible minds of the researcher-makers of the Information Sciences Institute at the University of Southern California. Karma will likely handle this critical component of the technology stack that implements the FactMiners social-game ecosystem.

There are many reasons I had to explore the embedded metamodel subgraph design pattern for Neo4j databases in the first series of FactMiners ecosystem design documents.

My interest is grounded in my experience in the 1990's developing a pair of complementary Distributed Smalltalk frameworks to do what we called "executable business models." The basic idea was that if we came up with a super-elegant metamodel about how to do business processes AND STUCK TO IT no matter what on the server/executing-model side of things then the Desktop Visualization framework could dynamically generate what customer/users mistook for "applications." We found INCREDIBLE leverage in design-to-implementation and stakeholder buy-in (as what we built made sense to them... it was their mental model embodied in software).

We can bring this kind of leverage to graph databases, and I hope Neo4j will be the pioneer leader in this, by coming up with a community standard for an embedded metamodel subgraph feature.