Informledge System: A Modified Knowledge Network with Autonomous Nodes using Multi-lateral Links

TL;DR

The paper proposes the Informledge System, a theoretical knowledge network with autonomous nodes and multi-lateral links, aiming to emulate human-like knowledge integration and retrieval in AI systems.

Contribution

It introduces a novel theoretical model of a knowledge network with autonomous nodes and multi-lateral links for efficient knowledge encoding and cross-linking.

Findings

Conceptual framework for knowledge encoding

Autonomous knowledge units with reasoning links

Potential for scalable knowledge integration

Abstract

Research in the field of Artificial Intelligence is continually progressing to simulate the human knowledge into automated intelligent knowledge base, which can encode and retrieve knowledge efficiently along with the capability of being is consistent and scalable at all times. However, there is no system at hand that can match the diversified abilities of human knowledge base. In this position paper, we put forward a theoretical model of a different system that intends to integrate pieces of knowledge, Informledge System (ILS). ILS would encode the knowledge, by virtue of knowledge units linked across diversified domains. The proposed ILS comprises of autonomous knowledge units termed as Knowledge Network Node (KNN), which would help in efficient cross-linking of knowledge units to encode fresh knowledge. These links are reasoned and inferred by the Parser and Link Manager, which are part of KNN.