Correlating and Cross-linking Knowledge Threads in Informledge System
  for Creating New Knowledge
  There has been a considerable advance in computing, to mimic the way in which
the brain tries to comprehend and structure the information to retrieve
meaningful knowledge. It is identified that neuronal entities hold whole of the
knowledge that the species makes use of. We intended to develop a modified
knowledge based system, termed as Informledge System (ILS) with autonomous
nodes and intelligent links that integrate and structure the pieces of
knowledge. We conceive that every piece of knowledge is a cluster of
cross-linked and correlated structure. In this paper, we put forward the theory
of the nodes depicting concepts, referred as Entity Concept State which in turn
is dealt with Concept State Diagrams (CSD). This theory is based on an abstract
framework provided by the concepts. The framework represents the ILS as the
weighted graph where the weights attached with the linked nodes help in
knowledge retrieval by providing the direction of connectivity of autonomous
nodes present in knowledge thread traversal. Here for the first time in the
process of developing Informledge, we apply tenor computation for creating
intelligent combinatorial knowledge with cross mutation to create fresh
knowledge which looks to be the fundamentals of a typical thought process.
