In this article I'll list the requisites for a successful knowledge management technology, discuss in more detail the shortcomings of First Generation Knowledge Management Technology and structure-based approaches, and address in more detail how a hyperspatial semantic index works.

We leverage how we work
2G-KM (Second Generation Knowledge Management) takes a unique approach. It leverages concepts from cognitive science, with particular emphasis on incremental learning, which permits users to harness InfoGlut as food for knowledge on a real-time basis. The design goals of 2G-KM technology should be simple, clear, and powerful:

• No manual intervention: must be fully automatic as a learning, sharing, and decision making service;

• Must make InfoGlut self-simplifying, self-organizing, self-describing, and self-aware, so it can organize itself responsively and flexibly to specific needs of individual users;

• Must support incremental indexing, so that each document is indexed autonomously into large knowledge indexes without need for the index to be reorganized to maintain optimal response, performance and currency;

• Must support massive scalability;

• Must be reflexive in real-time so it can read and react independently and responsively to meaningful relationship pattern signatures;

• Must be distributed so it can work with local and remote instances in a plug and play fashion;

• Must be able to work with and without a user interface so that knowledge-enabled applications can be bolted onto existing and legacy products and services for immediate payback;

• Must provide an easy-to-use development interface so it can easily be integrated with leading industry products;

• Must support and leverage industry standards;

• Must be multilingual;

• Must support human learning and knowledge sharing;

• Must remove the burden of manual data organization from the decision and learning process so corporations can clear the InfoGlut logjam with knowledge automation levers.