An algebraic expert system with neural network concepts for cyber, big data and data migration

This paper describes a machine assistance approach to grading decisions for values that might be missing or need validation, using a mathematical algebraic form of an Expert System, instead of the traditional textual or logic forms and builds a neural network computational graph structure. This Experts System approach is also structured into a neural network like format of: input, hidden and output layers that provide a structured approach to the knowledge-base organization, this provides a useful abstraction for reuse for data migration applications in big data, Cyber and relational databases. The approach is further enhanced with a Bayesian probability tree approach to grade the confidences of value probabilities, instead of the traditional grading of the rule probabilities, and estimates the most probable value in light of all evidence presented. This is ground work for a Machine Learning (ML) experts system approach in a form that is closer to a Neural Network node structure. The paper demonstrates the conversion between a Bayesian Network form of a probability tree toward a neural network computational graph structure. The proposed method also builds the Bayesian Network probability tree node structure from algebraic knowledgebase rules, and this forms a conversion from algebraic knowledgebase rules to the neural network computational graph structure. But importantly the semantics held within each node are a complete singular alternative permutation, rather than in a neural network where the semantic of a permutation is distributed over many nodes and may be overlapped with other permutations. This provides a node structure that is therefore potentially easier to validate and review while more commutable to Neural Network structures.