Correlating and Cross-linking Knowledge Threads in Informledge System for Creating New Knowledge

TL;DR

This paper introduces the Informledge System (ILS), a knowledge network with autonomous nodes and intelligent links, inspired by brain structures, to enhance knowledge retrieval and generate new insights through cross-linking and mutation.

Contribution

It presents a novel framework for a knowledge system with autonomous nodes and intelligent links, incorporating concepts like Entity Concept State and Concept State Diagrams, and applies tenor computation for knowledge creation.

Findings

Weighted graph model for knowledge retrieval

Application of tenor computation for knowledge mutation

First implementation of cross-linking for new knowledge generation

Abstract

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.