Aligning Knowledge Graphs Generated by Neural Networks with Human-Provided Knowledge for Improved Training and Interpretability

This paper proposes a method that utilizes knowledge graphs and Vector Symbolic Architecture (VSA) to enable bidirectional translation between neural network vectors and concept-level knowledge, allowing for the alignment of knowledge generated by neural networks with human-provided knowledge to enhance network training and interpretability.